Journal ArticleDOI
Unstructured Text Enhanced Open-Domain Dialogue System: A Systematic Survey
TLDR
Incorporating external knowledge into dialogue generation has been proven to benefit the performance of an open-domain Dialogue System (DS), such as generating informative or stylized responses.Abstract:
Incorporating external knowledge into dialogue generation has been proven to benefit the performance of an open-domain Dialogue System (DS), such as generating informative or stylized responses, co...read more
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The Web as a Knowledge-base for Answering Complex Questions
Alon Talmor,Jonathan Berant +1 more
TL;DR: This paper proposes to decompose complex questions into a sequence of simple questions, and compute the final answer from the sequence of answers, and empirically demonstrates that question decomposition improves performance from 20.8 precision@1 to 27.5 precision @1 on this new dataset.
Proceedings ArticleDOI
Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots
TL;DR: Three tasks in Doc2Bot are proposed: dialog state tracking to track user intentions, dialog policy learning to plan system actions and contents, and response generation which generates responses based on the outputs of the dialog policy.
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A Knowledge-Grounded Multimodal Search-Based Conversational Agent
TL;DR: This work introduces a knowledge-grounded multimodal conversational model where an encoded knowledge base (KB) representation is appended to the decoder input and substantially outperforms strong baselines in terms of text-based similarity measures.
Journal ArticleDOI
Capture Salient Historical Information: A Fast and Accurate Non-autoregressive Model for Multi-turn Spoken Language Understanding
TL;DR: A novel model for multi-turn SLU named Salient History Attention with Layer-Refined Transformer (SHA-LRT) is proposed, which composes of an SHA module, a Layer- refined Mechanism (LRM), and a Slot Label Generation (SLG) task.
Journal ArticleDOI
Retrieval-Augmented Response Generation for Knowledge-Grounded Conversation in the Wild
TL;DR: The authors proposed a retrieval-augmented response generation model that retrieves an appropriate range of documents relevant to both the topic and local context of a conversation and uses them for generating a knowledge-grounded response.
References
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Proceedings ArticleDOI
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.
Proceedings ArticleDOI
Effective Approaches to Attention-based Neural Machine Translation
TL;DR: A global approach which always attends to all source words and a local one that only looks at a subset of source words at a time are examined, demonstrating the effectiveness of both approaches on the WMT translation tasks between English and German in both directions.
Proceedings ArticleDOI
Deep contextualized word representations
Matthew E. Peters,Mark Neumann,Mohit Iyyer,Matt Gardner,Christopher Clark,Kenton Lee,Luke Zettlemoyer +6 more
TL;DR: This paper introduced a new type of deep contextualized word representation that models both complex characteristics of word use (e.g., syntax and semantics), and how these uses vary across linguistic contexts (i.e., to model polysemy).
Proceedings ArticleDOI
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Michael Lewis,Yinhan Liu,Naman Goyal,Marjan Ghazvininejad,Abdelrahman Mohamed,Omer Levy,Veselin Stoyanov,Luke Zettlemoyer +7 more
TL;DR: BART is presented, a denoising autoencoder for pretraining sequence-to-sequence models, which matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks.
Journal ArticleDOI
Word association norms, mutual information, and lexicography
Kenneth Church,Patrick Hanks +1 more
TL;DR: The proposed measure, the association ratio, estimates word association norms directly from computer readable corpora, making it possible to estimate norms for tens of thousands of words.