A Talker Ensemble: The University of Wroclaw’s Entry to the NIPS 2017 Conversational Intelligence Challenge (original) (raw)

Further Advances in Open Domain Dialog Systems in the Third Alexa Prize Socialbot Grand Challenge

2020

Building open domain conversational systems that allow users to have engaging conversations on topics of their choice is a challenging task. The Alexa Prize Socialbot Grand Challenge was launched in 2016 to tackle the problem of achieving natural, sustained, coherent and engaging open-domain dialogs. In the third iteration of the competition, university teams have moved the needle on the state of the art, bringing together common sense knowledge representations, neural response generation models, NLU systems enhanced by large-scale transformer models and improved dialog policies to switch between graph-based representations or retrieval-based or templated dialog fragments, along with generated responses. The Third Socialbot Grand Challenge included an improved version of the CoBot (conversational bot) toolkit from the prior competition, along with topic and dialog act detection models, conversation evaluators, and a sensitive content detection model so that the competing teams could...

Conversational AI for Natural Language Processing: An Review of ChatGPT

2023

ChatGPT is a conversational artificial intelligence model developed by OpenAI, which was introduced in 2019. It employs a transformer-based neural mesh to produce human being responses in real-time, allowing for natural language conversations with a machine. ChatGPT is instructed on huge quantities of data captured using the internet, making it knowledgeable in an extensive span of topics, from news & entertainment to politics and sports. This allows it to generate contextually relevant responses to questions and statements, making the conversation seem more lifelike. The model can be used in various applications, including customer service, personal assistants, and virtual assistants. ChatGPT has also shown promising results in generating creative content, such as jokes and poetry, showcasing its versatility and potential for future applications. This paper provides a comprehensive review of the existing literature on ChatGPT, highlighting its key advantages, such as improved accuracy and flexibility compared to traditional NLP tools, as well as its limitations and the need for further research to address potential ethical concerns. The review also highlights the potential for ChatGPT to be used in NLP applications, including question-answering and dialogue generation, and highlights the need for further research and development in these areas.

Viola: A Topic Agnostic Generate-and-Rank Dialogue System

Cornell University - arXiv, 2021

We present Viola, an open-domain dialogue system for spoken conversation that uses a topic-agnostic dialogue manager based on a simple generate-and-rank approach. Leveraging recent advances of generative dialogue systems powered by large language models, Viola fetches a batch of response candidates from various neural dialogue models trained with different datasets and knowledge-grounding inputs. Additional responses originating from template-based generators are also considered, depending on the user's input and detected entities. The hand-crafted generators build on a dynamic knowledge graph injected with rich content that is crawled from the web and automatically processed on a daily basis. Viola's response ranker is a fine-tuned polyencoder that chooses the best response given the dialogue history. While dedicated annotations for the polyencoder alone can indirectly steer it away from choosing problematic responses, we add rule-based safety nets to detect neural degeneration and a dedicated classifier to filter out offensive content. We analyze conversations that Viola took part in for the Alexa Prize Socialbot Grand Challenge 4 and discuss the strengths and weaknesses of our approach. Lastly, we suggest future work with a focus on curating conversation data specifcially for socialbots that will contribute towards a more robust data-driven socialbot. 4th Proceedings of Alexa Prize (Alexa Prize 2020).

Conversational AI: The Science Behind the Alexa Prize

2018

Conversational agents are exploding in popularity. However, much work remains in the area of social conversation as well as free-form conversation over a broad range of domains and topics. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar university competition where sixteen selected university teams were challenged to build conversational agents, known as socialbots, to converse coherently and engagingly with humans on popular topics such as Sports, Politics, Entertainment, Fashion and Technology for 20 minutes. The Alexa Prize offers the academic community a unique opportunity to perform research with a live system used by millions of users. The competition provided university teams with real user conversational data at scale, along with the user-provided ratings and feedback augmented with annotations by the Alexa team. This enabled teams to effectively iterate and make improvements throughout the competition while being e...

Data-Driven Dialogue Systems for Social Agents

In order to build dialogue systems to tackle the ambitious task of holding social conversations, we argue that we need a data-driven approach that includes insight into human conversational "chit-chat", and which incorporates different natural language processing modules. Our strategy is to analyze and index large corpora of social media data, including Twitter conversations, online debates, dialogues between friends, and blog posts, and then to couple this data retrieval with modules that perform tasks such as sentiment and style analysis, topic modeling, and summarization. We aim for personal assistants that can learn more nuanced human language, and to grow from task-oriented agents to more personable social bots.

DeepPavlov: Open-Source Library for Dialogue Systems

Proceedings of ACL 2018, System Demonstrations, 2018

Adoption of messaging communication and voice assistants has grown rapidly in the last years. This creates a demand for tools that speed up prototyping of featurerich dialogue systems. An open-source library DeepPavlov is tailored for development of conversational agents. The library prioritises efficiency, modularity, and extensibility with the goal to make it easier to develop dialogue systems from scratch and with limited data available. It supports modular as well as end-to-end approaches to implementation of conversational agents. Conversational agent consists of skills and every skill can be decomposed into components. Components are usually models which solve typical NLP tasks such as intent classification, named entity recognition or pre-trained word vectors. Sequence-to-sequence chitchat skill, question answering skill or task-oriented skill can be assembled from components provided in the library.

Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize

2018

Building open domain conversational systems that allow users to have engaging conversations on topics of their choice is a challenging task. Alexa Prize was launched in 2016 to tackle the problem of achieving natural, sustained, coherent and engaging open-domain dialogs. In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses. The 2018 competition also included the provision of a suite of tools and models to the competitors including the CoBot (conversational bot) toolkit, topic and dialog act detection models, conversation evaluators, and a sensitive content detection model so that the competing teams could focus on building knowledge-rich, coherent and engaging multi-turn dialog systems. This paper outlines the ...

Towards Open Domain Chatbots—A GRU Architecture for Data Driven Conversations

Lecture Notes in Computer Science, 2018

Understanding of textual content, such as topic and intent recognition, is a critical part of chatbots, allowing the chatbot to provide relevant responses. Although successful in several narrow domains, the potential diversity of content in broader and more open domains renders traditional pattern recognition techniques inaccurate. In this paper, we propose a novel deep learning architecture for content recognition that consists of multiple levels of gated recurrent units (GRUs). The architecture is designed to capture complex sentence structure at multiple levels of abstraction, seeking content recognition for very wide domains, through a distributed scalable representation of content. To evaluate our architecture, we have compiled 10 years of questions and answers from a youth information service, 200083 questions spanning a wide range of content, altogether 289 topics, involving law, health, and social issues. Despite the relatively open domain data set, our architecture is able to accurately categorize the 289 intents and topics. Indeed, it provides roughly an order of magnitude higher accuracy compared to content recognition techniques, such as SVM, Naive Bayes, random forest, and K-nearest neighbor, which all seem to fail on this challenging open domain dataset.

Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph

ArXiv, 2020

The third version of the open-domain dialogue system Alquist developed within the Alexa Prize 2020 competition is designed to conduct coherent and engaging conversations on popular topics. The main novel contribution is the introduction of a system leveraging an innovative approach based on a conversational knowledge graph and adjacency pairs. The conversational knowledge graph allows the system to utilize knowledge expressed during the dialogue in consequent turns and across conversations. Dialogue adjacency pairs divide the conversation into small conversational structures, which can be combined and allow the system to react to a wide range of user inputs flexibly. We discuss and describe Alquist's pipeline, data acquisition and processing, dialogue manager, NLG, knowledge aggregation, and a hierarchy of adjacency pairs. We present the experimental results of the individual parts of the system.

PeQA: A Massive Persian Question-Answering and Chatbot Dataset

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE), 2022

TA question-answering (QA) system is an application able to communicate with humans using natural language processing. Modelling a dialogue between humans and machines is considered one of the most important tasks of Artificial Intelligence (AI). Creating a Chatbot with a good performance in modelling human-machine conversations is still one of the unsolved challenges in this field. Although Chatbots have many applications, in general, they should understand users' meaning through their words and provide them with relevant answers. In the past, Chatbot architectures mainly relied on rules or statistical methods. With the advent of deep learning methods, trainable neural networks soon replaced the traditional models. These sorts of deep models are highly affected by the dataset that would be fed into them, and there is no big enough one available in the Persian language! We present a huge dataset of 14 million Persian tweets from tweeter that is meticulously processed to create a rich collection of 420,000 pairs of question-answer data. We also present modelling results on Transformers, including Sensibleness and Specificity Average (SSA) and the BLEU metric. We will release our dataset, modelling code, and models publicly 1 .