Compositional Phrase Alignment and Beyond (original) (raw)
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Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders
Cornell University - arXiv, 2022
Previous works have demonstrated the effectiveness of utilising pre-trained sentence encoders based on their sentence representations for meaning comparison tasks. Though such representations are shown to capture hidden syntax structures, the direct similarity comparison between them exhibits weak sensitivity to word order and structural differences in given sentences. A single similarity score further makes the comparison process hard to interpret. Therefore, we here propose to combine sentence encoders with an alignment component by representing each sentence as a list of predicate-argument spans (where their span representations are derived from sentence encoders), and decomposing the sentence-level meaning comparison into the alignment between their spans for paraphrase identification tasks. Empirical results show that the alignment component brings in both improved performance and interpretability for various sentence encoders. After closer investigation, the proposed approach indicates increased sensitivity to structural difference and enhanced ability to distinguish non-paraphrases with high lexical overlap.
A phrase-based alignment model for natural language inference
Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08, 2008
The alignment problem-establishing links between corresponding phrases in two related sentences-is as important in natural language inference (NLI) as it is in machine translation (MT). But the tools and techniques of MT alignment do not readily transfer to NLI, where one cannot assume semantic equivalence, and for which large volumes of bitext are lacking. We present a new NLI aligner, the MANLI system, designed to address these challenges. It uses a phrase-based alignment representation, exploits external lexical resources, and capitalizes on a new set of supervised training data. We compare the performance of MANLI to existing NLI and MT aligners on an NLI alignment task over the well-known Recognizing Textual Entailment data. We show that MANLI significantly outperforms existing aligners, achieving gains of 6.2% in F 1 over a representative NLI aligner and 10.5% over GIZA++.
Monolingual Phrase Alignment on Parse Forests
We propose an efficient method to conduct phrase alignment on parse forests for paraphrase detection. Unlike previous studies, our method identifies syntactic paraphrases under linguistically motivated grammar. In addition, it allows phrases to non-compositionally align to handle paraphrases with non-homographic phrase correspondences. A dataset that provides gold parse trees and their phrase alignments is created. The experimental results confirm that the proposed method conducts highly accurate phrase alignment compared to human performance.
SPADE: Evaluation Dataset for Monolingual Phrase Alignment
2018
We create the SPADE (Syntactic Phrase Alignment Dataset for Evaluation) for systematic research on syntactic phrase alignment in paraphrasal sentences. This is the first dataset to shed lights on syntactic and phrasal paraphrases under linguistically motivated grammar. Existing datasets available for evaluation on phrasal paraphrase detection define the unit of phrase as simply sequence of words without syntactic structures due to difficulties caused by the non-homographic nature of phrase correspondences in sentential paraphrases. Different from these, the SPADE provides annotations of gold parse trees by a linguistic expert and gold phrase alignments identified by three annotators. Consequently, 20, 276 phrases are extracted from 201 sentential paraphrases, on which 15, 721 alignments are obtained that at least one annotator regarded as paraphrases. The SPADE is available at Linguistic Data Consortium for future research on paraphrases. In addition, two metrics are proposed to eva...
CA-RNN: Using Context-Aligned Recurrent Neural Networks for Modeling Sentence Similarity
Proceedings of the AAAI Conference on Artificial Intelligence
The recurrent neural networks (RNNs) have shown good performance for sentence similarity modeling in recent years. Most RNNs focus on modeling the hidden states based on the current sentence, while the context information from the other sentence is not well investigated during the hidden state generation. In this paper, we propose a context-aligned RNN (CA-RNN) model, which incorporates the contextual information of the aligned words in a sentence pair for the inner hidden state generation. Specifically, we first perform word alignment detection to identify the aligned words in the two sentences. Then, we present a context alignment gating mechanism and embed it into our model to automatically absorb the aligned words' context for the hidden state update. Experiments on three benchmark datasets, namely TREC-QA and WikiQA for answer selection and MSRP for paraphrase identification, show the great advantages of our proposed model. In particular, we achieve the new state-of-the-art...
A Joint Phrasal and Dependency Model for Paraphrase Alignment
Monolingual alignment is frequently required for natural language tasks that involve similar or comparable sentences. We present a new model for monolingual alignment in which the score of an alignment decomposes over both the set of aligned phrases as well as a set of aligned dependency arcs. Optimal alignments under this scoring function are decoded using integer linear programming while model parameters are learned using standard structured prediction approaches. We evaluate our joint aligner on the Edinburgh paraphrase corpus and show significant gains over a Meteor baseline and a state-of-the-art phrase-based aligner.
Relation alignment for textual entailment recognition
2009
Abstract We present an approach to textual entailment recognition, in which inference is based on a shallow semantic representation of relations (predicates and their arguments) in the text and hypothesis of the entailment pair, and in which specialized knowledge is encapsulated in modular components with very simple interfaces. We propose an architecture designed to integrate different, unscaled Natural
Entailment Relation Aware Paraphrase Generation
Proceedings of the AAAI Conference on Artificial Intelligence
We introduce a new task of entailment relation aware paraphrase generation which aims at generating a paraphrase conforming to a given entailment relation (e.g. equivalent, forward entailing, or reverse entailing) with respect to a given input. We propose a reinforcement learning-based weakly-supervised paraphrasing system, ERAP, that can be trained using existing paraphrase and natural language inference (NLI) corpora without an explicit task-specific corpus. A combination of automated and human evaluations show that ERAP generates paraphrases conforming to the specified entailment relation and are of good quality as compared to the baselines and uncontrolled paraphrasing systems. Using ERAP for augmenting training data for downstream textual entailment task improves performance over an uncontrolled paraphrasing system, and introduces fewer training artifacts, indicating the benefit of explicit control during paraphrasing.
We describe our system (DT Team) submitted at SemEval-2017 Task 1, Semantic Textual Similarity (STS) challenge for English (Track 5). We developed three different models with various features including similarity scores calculated using word and chunk alignments, word/sentence embeddings, and Gaussian Mixture Model (GMM). The correlation between our system's output and the human judgments were up to 0.8536, which is more than 10% above baseline, and almost as good as the best performing system which was at 0.8547 correlation (the difference is just about 0.1%). Also, our system produced leading results when evaluated with a separate STS benchmark dataset. The word alignment and sentence embeddings based features were found to be very effective.
This paper describes the WOLVESAAR systems that participated in the English Semantic Textual Similarity (STS) task in SemEval-2016. We replicated the top systems from the last two editions of the STS task and extended the model using GloVe word embeddings and dense vector space LSTM based sentence representations. We compared the difference in performance of the replicated system and the extended variants. Our variants to the replicated system show improved correlation scores and all of our submissions outperform the median scores from all participating systems.