Conversational Semantic Parsing

• URL: http://arxiv.org/abs/2009.13655v1
• Date: Mon, 28 Sep 2020 22:08:00 GMT
• Title: Conversational Semantic Parsing
• Authors: Armen Aghajanyan, Jean Maillard, Akshat Shrivastava, Keith Diedrick, Mike Haeger, Haoran Li, Yashar Mehdad, Ves Stoyanov, Anuj Kumar, Mike Lewis, Sonal Gupta
• Abstract summary: Session-based properties such as co-reference resolution and context carryover are processed downstream in a pipelined system. We release a new session-based, compositional task-oriented parsing dataset of 20k sessions consisting of 60k utterances. We propose a new family of Seq2Seq models for the session-based parsing above, which achieve better or comparable performance to the current state-of-the-art on ATIS, SNIPS, TOP and DSTC2.
• Score: 50.954321571100294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the representation, the session-based properties such as co-reference resolution and context carryover are processed downstream in a pipelined system. In this paper, we propose a semantic representation for such task-oriented conversational systems that can represent concepts such as co-reference and context carryover, enabling comprehensive understanding of queries in a session. We release a new session-based, compositional task-oriented parsing dataset of 20k sessions consisting of 60k utterances. Unlike Dialog State Tracking Challenges, the queries in the dataset have compositional forms. We propose a new family of Seq2Seq models for the session-based parsing above, which achieve better or comparable performance to the current state-of-the-art on ATIS, SNIPS, TOP and DSTC2. Notably, we improve the best known results on DSTC2 by up to 5 points for slot-carryover.

Related papers

• Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation [62.275143240798236]
  Video semantic segmentation dataset has limited categories per video. Less than 10% of queries could be matched to receive meaningful gradient updates during VSS training. Our method achieves state-of-the-art performance on the latest challenging VSS benchmark VSPW without bells and whistles.
  arXiv  Detail & Related papers  (2023-09-14T20:31:06Z)
• Implicit Session Contexts for Next-Item Recommendations [27.250637187941532]
  Session contexts are not explicitly given in most datasets. We propose ISCON, which implicitly contextualizes sessions. ISCON has superior next-item prediction accuracy than state-of-the-art models.
  arXiv  Detail & Related papers  (2022-08-18T21:48:16Z)
• End-to-end Spoken Conversational Question Answering: Task, Dataset and Model [92.18621726802726]
  In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. We propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogue flows. Our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering.
  arXiv  Detail & Related papers  (2022-04-29T17:56:59Z)
• Dialogue Meaning Representation for Task-Oriented Dialogue Systems [51.91615150842267]
  We propose Dialogue Meaning Representation (DMR), a flexible and easily extendable representation for task-oriented dialogue. Our representation contains a set of nodes and edges with inheritance hierarchy to represent rich semantics for compositional semantics and task-specific concepts. We propose two evaluation tasks to evaluate different machine learning based dialogue models, and further propose a novel coreference resolution model GNNCoref for the graph-based coreference resolution task.
  arXiv  Detail & Related papers  (2022-04-23T04:17:55Z)
• Unsupervised Conversation Disentanglement through Co-Training [30.304609312675186]
  We explore to train a conversation disentanglement model without referencing any human annotations. Our method is built upon a deep co-training algorithm, which consists of two neural networks. For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence.
  arXiv  Detail & Related papers  (2021-09-07T17:05:18Z)
• Pairwise Representation Learning for Event Coreference [73.10563168692667]
  We develop a Pairwise Representation Learning (PairwiseRL) scheme for the event mention pairs. Our representation supports a finer, structured representation of the text snippet to facilitate encoding events and their arguments. We show that PairwiseRL, despite its simplicity, outperforms the prior state-of-the-art event coreference systems on both cross-document and within-document event coreference benchmarks.
  arXiv  Detail & Related papers  (2020-10-24T06:55:52Z)
• Conversational Semantic Parsing for Dialog State Tracking [7.739184049716928]
  We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. We present TreeDST, a dataset of 27k conversations annotated with tree-structured dialog states and system acts.
  arXiv  Detail & Related papers  (2020-10-24T04:10:32Z)
• A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining [52.11221075687124]
  We propose a novel abstractive summary network that adapts to the meeting scenario. We design a hierarchical structure to accommodate long meeting transcripts and a role vector to depict the difference among speakers. Our model outperforms previous approaches in both automatic metrics and human evaluation.
  arXiv  Detail & Related papers  (2020-04-04T21:00:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.