Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings
01 Jul 2017-Vol. 1, pp 1766-1776
TL;DR: This paper proposed a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses to model both structured knowledge and unstructured language, which exhibited interesting lexical, semantic, and strategic elements.
Abstract: We study a symmetric collaborative dialogue setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.
Citations
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21 Aug 2018
TL;DR: QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as it shows in a detailed qualitative evaluation.
Abstract: We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at http://quac.ai.
690 citations
Proceedings Article•
09 Feb 2017TL;DR: The authors generalize the Seq2Seq approach by conditioning responses on both conversation history and external "facts", allowing the model to be versatile and applicable in an open-domain setting.
Abstract: Neural network models are capable of generating extremely natural sounding conversational interactions. However, these models have been mostly applied to casual scenarios (e.g., as “chatbots”) and have yet to demonstrate they can serve in more useful conversational applications. This paper presents a novel, fully data-driven, and knowledge-grounded neural conversation model aimed at producing more contentful responses. We generalize the widely-used Sequence-to-Sequence (Seq2Seq) approach by conditioning responses on both conversation history and external “facts”, allowing the model to be versatile and applicable in an open-domain setting. Our approach yields significant improvements over a competitive Seq2Seq baseline. Human judges found that our outputs are significantly more informative.
376 citations
16 Jun 2017
TL;DR: For the first time, it is shown it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states, and this technique dramatically improves performance.
Abstract: Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available.
299 citations
04 Jun 2019
TL;DR: The authors proposed an attention-based feature embedding that captures both entity and relation features in any given entity's neighborhood, and encapsulated relation clusters and multi-hop relations in their model.
Abstract: The recent proliferation of knowledge graphs
(KGs) coupled with incomplete or partial information, in the form of missing relations
(links) between entities, has fueled a lot of
research on knowledge base completion (also
known as relation prediction). Several recent works suggest that convolutional neural
network (CNN) based models generate richer
and more expressive feature embeddings and
hence also perform well on relation prediction.
However, we observe that these KG embeddings treat triples independently and thus fail
to cover the complex and hidden information
that is inherently implicit in the local neighborhood surrounding a triple. To this effect, our
paper proposes a novel attention-based feature
embedding that captures both entity and relation features in any given entity’s neighborhood. Additionally, we also encapsulate relation clusters and multi-hop relations in our
model. Our empirical study offers insights
into the efficacy of our attention-based model
and we show marked performance gains in
comparison to state-of-the-art methods on all
datasets.
298 citations
01 Jan 2018
TL;DR: In this paper, a multi-hop knowledge graph reasoning with reward shaping is proposed, where the reasoning module receives a reward when a node corresponding to an observed answer is reached, and the reward is identified by a reward shaping network when the node not corresponding to an answer is not reached.
Abstract: Approaches for multi-hop knowledge graph reasoning with reward shaping include a system and method of training a system to search relational paths in a knowledge graph The method includes identifying, using an reasoning module, a plurality of first outgoing links from a current node in a knowledge graph, masking, using the reasoning module, one or more links from the plurality of first outgoing links to form a plurality of second outgoing links, rewarding the reasoning module with a reward of one when a node corresponding to an observed answer is reached, and rewarding the reasoning module with a reward identified by a reward shaping network when a node not corresponding to an observed answer is reached In some embodiments, the reward shaping network is pre-trained
282 citations
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1,812 citations
Proceedings Article•
12 Feb 2016TL;DR: The authors extend the hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and backoff n-gram models.
Abstract: We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and backoff n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.
1,533 citations