A Benchmark for Understanding and Generating Dialogue between Characters in Stories

• URL: http://arxiv.org/abs/2209.08524v1
• Date: Sun, 18 Sep 2022 10:19:04 GMT
• Title: A Benchmark for Understanding and Generating Dialogue between Characters in Stories
• Authors: Jianzhu Yao, Ziqi Liu, Jian Guan, Minlie Huang
• Abstract summary: We present the first study to explore whether machines can understand and generate dialogue in stories. We propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition. We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DialStory.
• Score: 75.29466820496913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many classical fairy tales, fiction, and screenplays leverage dialogue to advance story plots and establish characters. We present the first study to explore whether machines can understand and generate dialogue in stories, which requires capturing traits of different characters and the relationships between them. To this end, we propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition, i.e., generating missing dialogue turns and predicting speakers for specified dialogue turns, respectively. We build a new dataset DialStory, which consists of 105k Chinese stories with a large amount of dialogue weaved into the plots to support the evaluation. We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DialStory. Furthermore, we propose to learn explicit character representations to improve performance on these tasks. Extensive experiments and case studies show that our approach can generate more coherent and informative dialogue, and achieve higher speaker recognition accuracy than strong baselines.

Related papers

• Towards human-like spoken dialogue generation between AI agents from written dialogue [8.4989907582951]
  This study proposes CHATS - CHatty Agents Text-to-Speech - a discrete token-based system designed to generate spoken dialogues based on written dialogues. Our system can generate speech for both the speaker side and the listener side simultaneously, using only the transcription from the speaker side.
  arXiv  Detail & Related papers  (2023-10-02T11:03:20Z)
• Multi-turn Dialogue Comprehension from a Topic-aware Perspective [70.37126956655985]
  This paper proposes to model multi-turn dialogues from a topic-aware perspective. We use a dialogue segmentation algorithm to split a dialogue passage into topic-concentrated fragments in an unsupervised way. We also present a novel model, Topic-Aware Dual-Attention Matching (TADAM) Network, which takes topic segments as processing elements.
  arXiv  Detail & Related papers  (2023-09-18T11:03:55Z)
• STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension [42.57581945778631]
  Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing. We propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization. We show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.
  arXiv  Detail & Related papers  (2022-12-24T04:39:54Z)
• Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension [48.483910831143724]
  Comprehending a dialogue requires a model to capture diverse kinds of key information in the utterances. We develop a novel narrative-guided pre-training strategy that learns by narrating the key information from a dialogue input.
  arXiv  Detail & Related papers  (2022-03-19T05:20:25Z)
• Structural Modeling for Dialogue Disentanglement [43.352833140317486]
  Multi-party dialogue context Tangled multi-party dialogue context leads to challenges for dialogue reading comprehension. This work designs a novel model to disentangle multi-party history into threads, by taking dialogue structure features into account.
  arXiv  Detail & Related papers  (2021-10-15T11:28:43Z)
• "How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations [87.95711406978157]
  This work presents a new benchmark on spoken task-oriented conversations. We study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling. Our data set enables speech-based benchmarking of task-oriented dialogue systems.
  arXiv  Detail & Related papers  (2021-09-28T04:51:04Z)
• Graph Based Network with Contextualized Representations of Turns in Dialogue [0.0]
  Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. We propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues.
  arXiv  Detail & Related papers  (2021-09-09T03:09:08Z)
• Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
  We review the previous methods from the perspective of dialogue modeling. We discuss three typical patterns of dialogue modeling that are widely-used in dialogue comprehension tasks.
  arXiv  Detail & Related papers  (2021-03-04T15:50:17Z)
• Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue [76.88174667929665]
  A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. In the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely. We propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history.
  arXiv  Detail & Related papers  (2020-09-14T15:07:19Z)

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.