Parisa Kordjamshidi - Academia.edu (original) (raw)

Papers by Parisa Kordjamshidi

arXiv (Cornell University), Sep 26, 2022

Understanding spatial and visual information is essential for a navigation agent who follows natu... more Understanding spatial and visual information is essential for a navigation agent who follows natural language instructions. The current Transformer-based VLN agents entangle the orientation and vision information, which limits the gain from the learning of each information source. In this paper, we design a neural agent with explicit Orientation and Vision modules. Those modules learn to ground spatial information and landmark mentions in the instructions to the visual environment more effectively. To strengthen the spatial reasoning and visual perception of the agent, we design specific pre-training tasks to feed and better utilize the corresponding modules in our final navigation model. We evaluate our approach on both Room2room (R2R) and Room4room (R4R) datasets and achieve the state of the art results on both benchmarks.

CLEF (Working Notes), 2017

The extraction of spatial semantics is important in many real-world applications such as geograph... more The extraction of spatial semantics is important in many real-world applications such as geographical information systems, robotics and navigation, semantic search, etc. Moreover, spatial semantics are the most relevant semantics related to the visualization of language. The goal of multimodal spatial role labeling task is to extract spatial information from free text while exploiting accompanying images. This task is a multimodal extension of spatial role labeling task which has been previously introduced as a semantic evaluation task in the SemEval series. The multimodal aspect of the task makes it appropriate for the CLEF lab series. In this paper, we provide an overview of the task of multimodal spatial role labeling. We describe the task, sub-tasks, corpora, annotations, evaluation metrics, and the results of the baseline and the task participant.

In this paper, we investigate the problem of vision and language navigation. To solve this proble... more In this paper, we investigate the problem of vision and language navigation. To solve this problem, grounding the landmarks and spatial relations in the textual instructions into visual modality is important. We propose a neural agent named Explicit Object Relation Alignment Agent (EXOR), to explicitly align the spatial information in both instruction and the visual environment, including landmarks and spatial relationships between the agent and landmarks. Empirically, our proposed method surpasses the baseline by a large margin on the R2R dataset. We provide a comprehensive analysis to show our model's spatial reasoning ability and explainability.

arXiv (Cornell University), Feb 17, 2023

Language understanding is essential for the navigation agent to follow instructions. We observe t... more Language understanding is essential for the navigation agent to follow instructions. We observe two kinds of issues in the instructions that can make the navigation task challenging: 1. The mentioned landmarks are not recognizable by the navigation agent due to the different vision abilities of the instructor and the modeled agent. 2. The mentioned landmarks are applicable to multiple targets, thus not distinctive for selecting the target among the candidate viewpoints. To deal with these issues, we design a translator module for the navigation agent to convert the original instructions into easy-to-follow sub-instruction representations at each step. The translator needs to focus on the recognizable and distinctive landmarks based on the agent's visual abilities and the observed visual environment. To achieve this goal, we create a new synthetic sub-instruction dataset and design specific tasks to train the translator and the navigation agent. We evaluate our approach on Room2Room (R2R), Room4room (R4R), and Room2Room Last (R2R-Last) datasets and achieve state-of-the-art results on multiple benchmarks.

Language Resources and Evaluation, May 1, 2016

When designing Natural Language Processing (NLP) applications that use Machine Learning (ML) tech... more When designing Natural Language Processing (NLP) applications that use Machine Learning (ML) techniques, feature extraction becomes a significant part of the development effort, whether developing a new application or attempting to reproduce results reported for existing NLP tasks. We present EDISON, a Java library of feature generation functions used in a suite of state-of-the-art NLP tools, based on a set of generic NLP data structures. These feature extractors populate simple data structures encoding the extracted features, which the package can also serialize to an intuitive JSON file format that can be easily mapped to formats used by ML packages. EDISON can also be used programmatically with JVM-based (Java/Scala) NLP software to provide the feature extractor input. The collection of feature extractors is organised hierarchically and a simple search interface is provided. In this paper we include examples that demonstrate the versatility and ease-of-use of the EDISON feature extraction suite to show that this can significantly reduce the time spent by developers on feature extraction design for NLP systems. The library is publicly hosted at https://github.com/IllinoisCogComp/illinois-cogcomp-nlp/, and we hope that other NLP researchers will contribute to the set of feature extractors. In this way, the community can help simplify reproduction of published results and the integration of ideas from diverse sources when developing new and improved NLP applications.

This work is on a previously formalized semantic evaluation task of spatial role labeling (SpRL) ... more This work is on a previously formalized semantic evaluation task of spatial role labeling (SpRL) that aims at extraction of formal spatial meaning from text. Here, we report the results of initial efforts towards exploiting visual information in the form of images to help spatial language understanding. We discuss the way of designing new models in the framework of declarative learning-based programming (DeLBP). The DeLBP framework facilitates combining modalities and representing various data in a unified graph. The learning and inference models exploit the structure of the unified graph as well as the global first order domain constraints beyond the data to predict the semantics which forms a structured meaning representation of the spatial context. Continuous representations are used to relate the various elements of the graph originating from different modalities. We improved over the state-of-the-art results on SpRL.

arXiv (Cornell University), May 22, 2023

Recent research on transformer-based language models investigates their reasoning ability over lo... more Recent research on transformer-based language models investigates their reasoning ability over logical rules expressed in natural language text. However, their logic is not yet well-understood as we cannot explain the abstractions made by the models that help them in reasoning. These models are criticized for merely memorizing complex patterns in the data, which often creates issues for their generalizability in unobserved situations. In this work, we analyze the use of probabilistic logical rules in transformer-based language models. In particular, we propose a new approach, Probabilistic Constraint Training (PCT), that explicitly models probabilistic logical reasoning by imposing the rules of reasoning as constraints during training. We create a new QA benchmark for evaluating probabilistic reasoning over uncertain textual rules, which creates instance-specific rules, unlike the only existing relevant benchmark. Experimental results show that our proposed technique improves the base language models' accuracy and explainability when probabilistic logical reasoning is required for question answering. Moreover, we show that the learned probabilistic reasoning abilities are transferable to novel situations.

arXiv (Cornell University), Jun 18, 2019

Data-driven approaches are becoming more common as problem-solving techniques in many areas of re... more Data-driven approaches are becoming more common as problem-solving techniques in many areas of research and industry. In most cases, machine learning models are the key component of these solutions, but a solution involves multiple such models, along with significant levels of reasoning with the models' output and input. Current technologies do not make such techniques easy to use for application experts who are not fluent in machine learning nor for machine learning experts who aim at testing ideas and models on real-world data in the context of the overall AI system. We review key efforts made by various AI communities to provide languages for high-level abstractions over learning and reasoning techniques needed for designing complex AI systems. We classify the existing frameworks based on the type of techniques and the data and knowledge representations they use, provide a comparative study of the way they address the challenges of programming real-world applications, and highlight some shortcomings and future directions.

arXiv (Cornell University), May 27, 2021

This paper addresses the challenge of learning to do procedural reasoning over text to answer "Wh... more This paper addresses the challenge of learning to do procedural reasoning over text to answer "What if..." questions. We propose a novel relational gating network that learns to filter the key entities and relationships and learns contextual and cross representations of both procedure and question for finding the answer. Our relational gating network contains an entity gating module, relation gating module, and contextual interaction module. These modules help in solving the "What if..." reasoning problem. We show that modeling pairwise relationships helps to capture higher-order relations and find the line of reasoning for causes and effects in the procedural descriptions. Our proposed approach achieves the state-of-the-art results on the WIQA dataset.

BMC Bioinformatics, Apr 25, 2015

Background: We aim to automatically extract species names of bacteria and their locations from we... more Background: We aim to automatically extract species names of bacteria and their locations from webpages. This task is important for exploiting the vast amount of biological knowledge which is expressed in diverse natural language texts and putting this knowledge in databases for easy access by biologists. The task is challenging and the previous results are far below an acceptable level of performance, particularly for extraction of localization relationships. Therefore, we aim to design a new system for such extractions, using the framework of structured machine learning techniques. Results: We design a new model for joint extraction of biomedical entities and the localization relationship. Our model is based on a spatial role labeling (SpRL) model designed for spatial understanding of unrestricted text. We extend SpRL to extract discourse level spatial relations in the biomedical domain and apply it on the BioNLP-ST 2013, BB-shared task. We highlight the main differences between general spatial language understanding and spatial information extraction from the scientific text which is the focus of this work. We exploit the text's structure and discourse level global features. Our model and the designed features substantially improve on the previous systems, achieving an absolute improvement of approximately 57 percent over F1 measure of the best previous system for this task. Conclusions: Our experimental results indicate that a joint learning model over all entities and relationships in a document outperforms a model which extracts entities and relationships independently. Our global learning model significantly improves the state-of-the-art results on this task and has a high potential to be adopted in other natural language processing (NLP) tasks in the biomedical domain.

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Recent research shows synthetic data as a source of supervision helps pretrained language models ... more Recent research shows synthetic data as a source of supervision helps pretrained language models (PLM) transfer learning to new target tasks/domains. However, this idea is less explored for spatial language. We provide two new data resources on multiple spatial language processing tasks. The first dataset is synthesized for transfer learning on spatial question answering (SQA) and spatial role labeling (SpRL). Compared to previous SQA datasets, we include a larger variety of spatial relation types and spatial expressions. Our data generation process is easily extendable with new spatial expression lexicons. The second one is a real-world SQA dataset with humangenerated questions built on an existing corpus with SPRL annotations. This dataset can be used to evaluate spatial language processing models in realistic situations. We show pretraining with automatically generated data significantly improves the SOTA results on several SQA and SPRL benchmarks, particularly when the training data in the target domain is small. 1 Spatial Reasoning and role labeling for Text UNnderstanding 2 We only consider explicit spatial semantics and the 6148

arXiv (Cornell University), Feb 16, 2023

Recent research has shown that integrating domain knowledge into deep learning architectures is e... more Recent research has shown that integrating domain knowledge into deep learning architectures is effective-it helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models. However, the research community is missing a convened benchmark for systematically evaluating knowledge integration methods. In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. In all cases, we model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints. We report the results of these models using a new set of extended evaluation criteria in addition to the task performances for a more in-depth analysis. This effort provides a framework for a more comprehensive and systematic comparison of constraint integration techniques and for identifying related research challenges. It will facilitate further research for alleviating some problems of state-of-the-art neural models.

arXiv (Cornell University), Feb 13, 2023

In this paper, we investigate whether symbolic semantic representations, extracted from deep sema... more In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help to reason over the states of involved entities in a procedural text. We consider a deep semantic parser (TRIPS) and semantic role labeling as two sources of semantic parsing knowledge. First, we propose PROPOLIS, a symbolic parsing-based procedural reasoning framework. Second, we integrate semantic parsing information into stateof-the-art neural models to conduct procedural reasoning. Our experiments indicate that explicitly incorporating such semantic knowledge improves procedural understanding. This paper presents new metrics for evaluating procedural reasoning tasks that clarify the challenges and identify differences among neural, symbolic, and integrated models.

Proceedings of the AAAI Conference on Artificial Intelligence

Recent research has shown that integrating domain knowledge into deep learning architectures is e... more Recent research has shown that integrating domain knowledge into deep learning architectures is effective; It helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models. However, the research community lacks a convened benchmark for systematically evaluating knowledge integration methods. In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. In all cases, we model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints. We report the results of these models using a new set of extended evaluation criteria in addition to the task performances for a more in-depth analysis. This effort provides a framework for a more comprehensive and systematic comparison of constraint integration techniques and for identifying related research challenges...

Cornell University - arXiv, Nov 10, 2022

The spread of misinformation is a prominent problem in today's society, and many researchers in a... more The spread of misinformation is a prominent problem in today's society, and many researchers in academia and industry are trying to combat it. Due to the vast amount of misinformation that is created every day, it is unrealistic to leave this task to human factcheckers. Data scientists and researchers have been working on automated misinformation detection for years, and it is still a challenging problem today. The goal of our research is to add a new level to automated misinformation detection; classifying segments of text with persuasive writing techniques in order to produce interpretable reasoning for why an article can be marked as misinformation. To accomplish this, we present a novel annotation scheme containing many common persuasive writing tactics, along with a dataset with human annotations accordingly. For this task, we make use of a RoBERTa model for text classification, due to its high performance in NLP. We develop several language model-based baselines and present the results of our persuasive strategy label predictions as well as the improvements these intermediate labels make in detecting misinformation and producing interpretable results.

Cornell University - arXiv, Sep 20, 2022

This work investigates the challenge of learning and reasoning for Commonsense Question Answering... more This work investigates the challenge of learning and reasoning for Commonsense Question Answering given an external source of knowledge in the form of a knowledge graph (KG). We propose a novel graph neural network architecture, called Dynamic Relevance Graph Network (DRGN). DRGN operates on a given KG subgraph based on the question and answers entities and uses the relevance scores between the nodes to establish new edges dynamically for learning node representations in the graph network. This explicit usage of relevance as graph edges has the following advantages, a) the model can exploit the existing relationships, re-scale the node weights, and influence the way the neighborhood nodes' representations are aggregated in the KG subgraph, b) It potentially recovers the missing edges in KG that are needed for reasoning. Moreover, as a byproduct, our model improves handling the negative questions due to considering the relevance between the question node and the graph entities. Our proposed approach shows competitive performance on two QA benchmarks, CommonsenseQA and Open-bookQA, compared to the state-of-the-art published results.

Computer Science

Neural Machine Translation (NMT) systems require a massive amount of Maintaining semantic relatio... more Neural Machine Translation (NMT) systems require a massive amount of Maintaining semantic relations between words during the translation process yields more accurate target-language output from Neural Machine Translation (NMT). Although difficult to achieve from training data alone, it is possible to leverage Knowledge Graphs (KGs) to retain source-language semantic relations in the corresponding target-language translation. The core idea is to use KG entity relations as embedding constraints to improve the mapping from source to target. This paper describes two embedding constraints, both of which employ Entity Linking (EL)---assigning a unique identity to entities---to associate words in training sentences with those in the KG: (1) a monolingual embedding constraint that supports an enhanced semantic representation of the source words through access to relations between entities in a KG; and (2) a bilingual embedding constraint that forces entity relations in the source-language t...

Proceedings of the AAAI Conference on Artificial Intelligence

Deep learning excels at learning task information from large amounts of data, but struggles with ... more Deep learning excels at learning task information from large amounts of data, but struggles with learning from declarative high-level knowledge that can be more succinctly expressed directly. In this work, we introduce PYLON, a neuro-symbolic training framework that builds on PyTorch to augment procedurally trained models with declaratively specified knowledge. PYLON lets users programmatically specify constraints as Python functions and compiles them into a differentiable loss, thus training predictive models that fit the data whilst satisfying the specified constraints. PYLON includes both exact as well as approximate compilers to efficiently compute the loss, employing fuzzy logic, sampling methods, and circuits, ensuring scalability even to complex models and constraints. Crucially, a guiding principle in designing PYLON is the ease with which any existing deep learning codebase can be extended to learn from constraints in a few lines code: a function that expresses the constrai...

Findings of the Association for Computational Linguistics: ACL 2022

We study the challenge of learning causal reasoning over procedural text to answer "What if..." q... more We study the challenge of learning causal reasoning over procedural text to answer "What if..." questions when external commonsense knowledge is required. We propose a novel multi-hop graph reasoning model to 1) efficiently extract a commonsense subgraph with the most relevant information from a large knowledge graph; 2) predict the causal answer by reasoning over the representations obtained from the commonsense subgraph and the contextual interactions between the questions and context. We evaluate our model on WIQA benchmark and achieve state-of-the-art performance compared to the recent models. Procedural Text: 1. A plant produces a seed. 2. The seed falls to the ground. 3. The seed is buried. 4. The seed germinates. 5. A plant grows. 6. The plant produces flowers. 7. The flowers produce more seeds Questions and Answers: 1. suppose plants will produce more seeds happens, how will it affect less plants. (A) More (B) Less (C) No effect 2. suppose the soil is rich in nutrients happens, how will it affect more seeds are produced. (A) More (B) Less (C) No effect 3. suppose The sun comes out happens, how will it affect less plants. (A) More (B) Less (C) No effect

arXiv (Cornell University), Sep 26, 2022

Understanding spatial and visual information is essential for a navigation agent who follows natu... more Understanding spatial and visual information is essential for a navigation agent who follows natural language instructions. The current Transformer-based VLN agents entangle the orientation and vision information, which limits the gain from the learning of each information source. In this paper, we design a neural agent with explicit Orientation and Vision modules. Those modules learn to ground spatial information and landmark mentions in the instructions to the visual environment more effectively. To strengthen the spatial reasoning and visual perception of the agent, we design specific pre-training tasks to feed and better utilize the corresponding modules in our final navigation model. We evaluate our approach on both Room2room (R2R) and Room4room (R4R) datasets and achieve the state of the art results on both benchmarks.

CLEF (Working Notes), 2017

The extraction of spatial semantics is important in many real-world applications such as geograph... more The extraction of spatial semantics is important in many real-world applications such as geographical information systems, robotics and navigation, semantic search, etc. Moreover, spatial semantics are the most relevant semantics related to the visualization of language. The goal of multimodal spatial role labeling task is to extract spatial information from free text while exploiting accompanying images. This task is a multimodal extension of spatial role labeling task which has been previously introduced as a semantic evaluation task in the SemEval series. The multimodal aspect of the task makes it appropriate for the CLEF lab series. In this paper, we provide an overview of the task of multimodal spatial role labeling. We describe the task, sub-tasks, corpora, annotations, evaluation metrics, and the results of the baseline and the task participant.

In this paper, we investigate the problem of vision and language navigation. To solve this proble... more In this paper, we investigate the problem of vision and language navigation. To solve this problem, grounding the landmarks and spatial relations in the textual instructions into visual modality is important. We propose a neural agent named Explicit Object Relation Alignment Agent (EXOR), to explicitly align the spatial information in both instruction and the visual environment, including landmarks and spatial relationships between the agent and landmarks. Empirically, our proposed method surpasses the baseline by a large margin on the R2R dataset. We provide a comprehensive analysis to show our model's spatial reasoning ability and explainability.

arXiv (Cornell University), Feb 17, 2023

Language understanding is essential for the navigation agent to follow instructions. We observe t... more Language understanding is essential for the navigation agent to follow instructions. We observe two kinds of issues in the instructions that can make the navigation task challenging: 1. The mentioned landmarks are not recognizable by the navigation agent due to the different vision abilities of the instructor and the modeled agent. 2. The mentioned landmarks are applicable to multiple targets, thus not distinctive for selecting the target among the candidate viewpoints. To deal with these issues, we design a translator module for the navigation agent to convert the original instructions into easy-to-follow sub-instruction representations at each step. The translator needs to focus on the recognizable and distinctive landmarks based on the agent's visual abilities and the observed visual environment. To achieve this goal, we create a new synthetic sub-instruction dataset and design specific tasks to train the translator and the navigation agent. We evaluate our approach on Room2Room (R2R), Room4room (R4R), and Room2Room Last (R2R-Last) datasets and achieve state-of-the-art results on multiple benchmarks.

Language Resources and Evaluation, May 1, 2016

When designing Natural Language Processing (NLP) applications that use Machine Learning (ML) tech... more When designing Natural Language Processing (NLP) applications that use Machine Learning (ML) techniques, feature extraction becomes a significant part of the development effort, whether developing a new application or attempting to reproduce results reported for existing NLP tasks. We present EDISON, a Java library of feature generation functions used in a suite of state-of-the-art NLP tools, based on a set of generic NLP data structures. These feature extractors populate simple data structures encoding the extracted features, which the package can also serialize to an intuitive JSON file format that can be easily mapped to formats used by ML packages. EDISON can also be used programmatically with JVM-based (Java/Scala) NLP software to provide the feature extractor input. The collection of feature extractors is organised hierarchically and a simple search interface is provided. In this paper we include examples that demonstrate the versatility and ease-of-use of the EDISON feature extraction suite to show that this can significantly reduce the time spent by developers on feature extraction design for NLP systems. The library is publicly hosted at https://github.com/IllinoisCogComp/illinois-cogcomp-nlp/, and we hope that other NLP researchers will contribute to the set of feature extractors. In this way, the community can help simplify reproduction of published results and the integration of ideas from diverse sources when developing new and improved NLP applications.

This work is on a previously formalized semantic evaluation task of spatial role labeling (SpRL) ... more This work is on a previously formalized semantic evaluation task of spatial role labeling (SpRL) that aims at extraction of formal spatial meaning from text. Here, we report the results of initial efforts towards exploiting visual information in the form of images to help spatial language understanding. We discuss the way of designing new models in the framework of declarative learning-based programming (DeLBP). The DeLBP framework facilitates combining modalities and representing various data in a unified graph. The learning and inference models exploit the structure of the unified graph as well as the global first order domain constraints beyond the data to predict the semantics which forms a structured meaning representation of the spatial context. Continuous representations are used to relate the various elements of the graph originating from different modalities. We improved over the state-of-the-art results on SpRL.

arXiv (Cornell University), May 22, 2023

Recent research on transformer-based language models investigates their reasoning ability over lo... more Recent research on transformer-based language models investigates their reasoning ability over logical rules expressed in natural language text. However, their logic is not yet well-understood as we cannot explain the abstractions made by the models that help them in reasoning. These models are criticized for merely memorizing complex patterns in the data, which often creates issues for their generalizability in unobserved situations. In this work, we analyze the use of probabilistic logical rules in transformer-based language models. In particular, we propose a new approach, Probabilistic Constraint Training (PCT), that explicitly models probabilistic logical reasoning by imposing the rules of reasoning as constraints during training. We create a new QA benchmark for evaluating probabilistic reasoning over uncertain textual rules, which creates instance-specific rules, unlike the only existing relevant benchmark. Experimental results show that our proposed technique improves the base language models' accuracy and explainability when probabilistic logical reasoning is required for question answering. Moreover, we show that the learned probabilistic reasoning abilities are transferable to novel situations.

arXiv (Cornell University), Jun 18, 2019

Data-driven approaches are becoming more common as problem-solving techniques in many areas of re... more Data-driven approaches are becoming more common as problem-solving techniques in many areas of research and industry. In most cases, machine learning models are the key component of these solutions, but a solution involves multiple such models, along with significant levels of reasoning with the models' output and input. Current technologies do not make such techniques easy to use for application experts who are not fluent in machine learning nor for machine learning experts who aim at testing ideas and models on real-world data in the context of the overall AI system. We review key efforts made by various AI communities to provide languages for high-level abstractions over learning and reasoning techniques needed for designing complex AI systems. We classify the existing frameworks based on the type of techniques and the data and knowledge representations they use, provide a comparative study of the way they address the challenges of programming real-world applications, and highlight some shortcomings and future directions.

arXiv (Cornell University), May 27, 2021

This paper addresses the challenge of learning to do procedural reasoning over text to answer "Wh... more This paper addresses the challenge of learning to do procedural reasoning over text to answer "What if..." questions. We propose a novel relational gating network that learns to filter the key entities and relationships and learns contextual and cross representations of both procedure and question for finding the answer. Our relational gating network contains an entity gating module, relation gating module, and contextual interaction module. These modules help in solving the "What if..." reasoning problem. We show that modeling pairwise relationships helps to capture higher-order relations and find the line of reasoning for causes and effects in the procedural descriptions. Our proposed approach achieves the state-of-the-art results on the WIQA dataset.

BMC Bioinformatics, Apr 25, 2015

Background: We aim to automatically extract species names of bacteria and their locations from we... more Background: We aim to automatically extract species names of bacteria and their locations from webpages. This task is important for exploiting the vast amount of biological knowledge which is expressed in diverse natural language texts and putting this knowledge in databases for easy access by biologists. The task is challenging and the previous results are far below an acceptable level of performance, particularly for extraction of localization relationships. Therefore, we aim to design a new system for such extractions, using the framework of structured machine learning techniques. Results: We design a new model for joint extraction of biomedical entities and the localization relationship. Our model is based on a spatial role labeling (SpRL) model designed for spatial understanding of unrestricted text. We extend SpRL to extract discourse level spatial relations in the biomedical domain and apply it on the BioNLP-ST 2013, BB-shared task. We highlight the main differences between general spatial language understanding and spatial information extraction from the scientific text which is the focus of this work. We exploit the text's structure and discourse level global features. Our model and the designed features substantially improve on the previous systems, achieving an absolute improvement of approximately 57 percent over F1 measure of the best previous system for this task. Conclusions: Our experimental results indicate that a joint learning model over all entities and relationships in a document outperforms a model which extracts entities and relationships independently. Our global learning model significantly improves the state-of-the-art results on this task and has a high potential to be adopted in other natural language processing (NLP) tasks in the biomedical domain.

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Recent research shows synthetic data as a source of supervision helps pretrained language models ... more Recent research shows synthetic data as a source of supervision helps pretrained language models (PLM) transfer learning to new target tasks/domains. However, this idea is less explored for spatial language. We provide two new data resources on multiple spatial language processing tasks. The first dataset is synthesized for transfer learning on spatial question answering (SQA) and spatial role labeling (SpRL). Compared to previous SQA datasets, we include a larger variety of spatial relation types and spatial expressions. Our data generation process is easily extendable with new spatial expression lexicons. The second one is a real-world SQA dataset with humangenerated questions built on an existing corpus with SPRL annotations. This dataset can be used to evaluate spatial language processing models in realistic situations. We show pretraining with automatically generated data significantly improves the SOTA results on several SQA and SPRL benchmarks, particularly when the training data in the target domain is small. 1 Spatial Reasoning and role labeling for Text UNnderstanding 2 We only consider explicit spatial semantics and the 6148

arXiv (Cornell University), Feb 16, 2023

Recent research has shown that integrating domain knowledge into deep learning architectures is e... more Recent research has shown that integrating domain knowledge into deep learning architectures is effective-it helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models. However, the research community is missing a convened benchmark for systematically evaluating knowledge integration methods. In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. In all cases, we model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints. We report the results of these models using a new set of extended evaluation criteria in addition to the task performances for a more in-depth analysis. This effort provides a framework for a more comprehensive and systematic comparison of constraint integration techniques and for identifying related research challenges. It will facilitate further research for alleviating some problems of state-of-the-art neural models.

arXiv (Cornell University), Feb 13, 2023

In this paper, we investigate whether symbolic semantic representations, extracted from deep sema... more In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help to reason over the states of involved entities in a procedural text. We consider a deep semantic parser (TRIPS) and semantic role labeling as two sources of semantic parsing knowledge. First, we propose PROPOLIS, a symbolic parsing-based procedural reasoning framework. Second, we integrate semantic parsing information into stateof-the-art neural models to conduct procedural reasoning. Our experiments indicate that explicitly incorporating such semantic knowledge improves procedural understanding. This paper presents new metrics for evaluating procedural reasoning tasks that clarify the challenges and identify differences among neural, symbolic, and integrated models.

Proceedings of the AAAI Conference on Artificial Intelligence

Recent research has shown that integrating domain knowledge into deep learning architectures is e... more Recent research has shown that integrating domain knowledge into deep learning architectures is effective; It helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models. However, the research community lacks a convened benchmark for systematically evaluating knowledge integration methods. In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. In all cases, we model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints. We report the results of these models using a new set of extended evaluation criteria in addition to the task performances for a more in-depth analysis. This effort provides a framework for a more comprehensive and systematic comparison of constraint integration techniques and for identifying related research challenges...

Cornell University - arXiv, Nov 10, 2022

The spread of misinformation is a prominent problem in today's society, and many researchers in a... more The spread of misinformation is a prominent problem in today's society, and many researchers in academia and industry are trying to combat it. Due to the vast amount of misinformation that is created every day, it is unrealistic to leave this task to human factcheckers. Data scientists and researchers have been working on automated misinformation detection for years, and it is still a challenging problem today. The goal of our research is to add a new level to automated misinformation detection; classifying segments of text with persuasive writing techniques in order to produce interpretable reasoning for why an article can be marked as misinformation. To accomplish this, we present a novel annotation scheme containing many common persuasive writing tactics, along with a dataset with human annotations accordingly. For this task, we make use of a RoBERTa model for text classification, due to its high performance in NLP. We develop several language model-based baselines and present the results of our persuasive strategy label predictions as well as the improvements these intermediate labels make in detecting misinformation and producing interpretable results.

Cornell University - arXiv, Sep 20, 2022

This work investigates the challenge of learning and reasoning for Commonsense Question Answering... more This work investigates the challenge of learning and reasoning for Commonsense Question Answering given an external source of knowledge in the form of a knowledge graph (KG). We propose a novel graph neural network architecture, called Dynamic Relevance Graph Network (DRGN). DRGN operates on a given KG subgraph based on the question and answers entities and uses the relevance scores between the nodes to establish new edges dynamically for learning node representations in the graph network. This explicit usage of relevance as graph edges has the following advantages, a) the model can exploit the existing relationships, re-scale the node weights, and influence the way the neighborhood nodes' representations are aggregated in the KG subgraph, b) It potentially recovers the missing edges in KG that are needed for reasoning. Moreover, as a byproduct, our model improves handling the negative questions due to considering the relevance between the question node and the graph entities. Our proposed approach shows competitive performance on two QA benchmarks, CommonsenseQA and Open-bookQA, compared to the state-of-the-art published results.

Computer Science

Neural Machine Translation (NMT) systems require a massive amount of Maintaining semantic relatio... more Neural Machine Translation (NMT) systems require a massive amount of Maintaining semantic relations between words during the translation process yields more accurate target-language output from Neural Machine Translation (NMT). Although difficult to achieve from training data alone, it is possible to leverage Knowledge Graphs (KGs) to retain source-language semantic relations in the corresponding target-language translation. The core idea is to use KG entity relations as embedding constraints to improve the mapping from source to target. This paper describes two embedding constraints, both of which employ Entity Linking (EL)---assigning a unique identity to entities---to associate words in training sentences with those in the KG: (1) a monolingual embedding constraint that supports an enhanced semantic representation of the source words through access to relations between entities in a KG; and (2) a bilingual embedding constraint that forces entity relations in the source-language t...

Proceedings of the AAAI Conference on Artificial Intelligence

Deep learning excels at learning task information from large amounts of data, but struggles with ... more Deep learning excels at learning task information from large amounts of data, but struggles with learning from declarative high-level knowledge that can be more succinctly expressed directly. In this work, we introduce PYLON, a neuro-symbolic training framework that builds on PyTorch to augment procedurally trained models with declaratively specified knowledge. PYLON lets users programmatically specify constraints as Python functions and compiles them into a differentiable loss, thus training predictive models that fit the data whilst satisfying the specified constraints. PYLON includes both exact as well as approximate compilers to efficiently compute the loss, employing fuzzy logic, sampling methods, and circuits, ensuring scalability even to complex models and constraints. Crucially, a guiding principle in designing PYLON is the ease with which any existing deep learning codebase can be extended to learn from constraints in a few lines code: a function that expresses the constrai...

Findings of the Association for Computational Linguistics: ACL 2022

We study the challenge of learning causal reasoning over procedural text to answer "What if..." q... more We study the challenge of learning causal reasoning over procedural text to answer "What if..." questions when external commonsense knowledge is required. We propose a novel multi-hop graph reasoning model to 1) efficiently extract a commonsense subgraph with the most relevant information from a large knowledge graph; 2) predict the causal answer by reasoning over the representations obtained from the commonsense subgraph and the contextual interactions between the questions and context. We evaluate our model on WIQA benchmark and achieve state-of-the-art performance compared to the recent models. Procedural Text: 1. A plant produces a seed. 2. The seed falls to the ground. 3. The seed is buried. 4. The seed germinates. 5. A plant grows. 6. The plant produces flowers. 7. The flowers produce more seeds Questions and Answers: 1. suppose plants will produce more seeds happens, how will it affect less plants. (A) More (B) Less (C) No effect 2. suppose the soil is rich in nutrients happens, how will it affect more seeds are produced. (A) More (B) Less (C) No effect 3. suppose The sun comes out happens, how will it affect less plants. (A) More (B) Less (C) No effect