Zero-Shot Dialogue Disentanglement by Self-Supervised Entangled Response Selection

• URL: http://arxiv.org/abs/2110.12646v2
• Date: Mon, 26 Jun 2023 22:44:04 GMT
• Title: Zero-Shot Dialogue Disentanglement by Self-Supervised Entangled Response Selection
• Authors: Ta-Chung Chi and Alexander I. Rudnicky
• Abstract summary: Dialogue disentanglement aims to group utterances in a long and multi-participant dialogue into threads. This is useful for discourse analysis and downstream applications such as dialogue response selection. We are the first to propose atextbfzero-shot dialogue disentanglement solution.
• Score: 79.37200787463917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dialogue disentanglement aims to group utterances in a long and multi-participant dialogue into threads. This is useful for discourse analysis and downstream applications such as dialogue response selection, where it can be the first step to construct a clean context/response set. Unfortunately, labeling all~\emph{reply-to} links takes quadratic effort w.r.t the number of utterances: an annotator must check all preceding utterances to identify the one to which the current utterance is a reply. In this paper, we are the first to propose a~\textbf{zero-shot} dialogue disentanglement solution. Firstly, we train a model on a multi-participant response selection dataset harvested from the web which is not annotated; we then apply the trained model to perform zero-shot dialogue disentanglement. Without any labeled data, our model can achieve a cluster F1 score of 25. We also fine-tune the model using various amounts of labeled data. Experiments show that with only 10\% of the data, we achieve nearly the same performance of using the full dataset\footnote{Code is released at \url{https://github.com/chijames/zero_shot_dialogue_disentanglement}}.

Related papers

• q2d: Turning Questions into Dialogs to Teach Models How to Search [11.421839177607147]
  We propose q2d: an automatic data generation pipeline that generates information-seeking dialogs from questions. Unlike previous approaches which relied on human written dialogs with search queries, our method allows to automatically generate query-based grounded dialogs with better control and scale.
  arXiv  Detail & Related papers  (2023-04-27T16:39:15Z)
• DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization [127.714919036388]
  DIONYSUS is a pre-trained encoder-decoder model for summarizing dialogues in any new domain. Our experiments show that DIONYSUS outperforms existing methods on six datasets.
  arXiv  Detail & Related papers  (2022-12-20T06:21:21Z)
• Prompting for a conversation: How to control a dialog model? [9.268682116424518]
  Dialog models are trained on a large amount of text, yet their responses need to be limited to a desired scope and style of a dialog agent. Because the datasets used to achieve the former contain language that is not compatible with the latter, pre-trained dialog models are fine-tuned on smaller curated datasets. In this paper we investigate if prompting can mitigate the above trade-off.
  arXiv  Detail & Related papers  (2022-09-22T14:59:55Z)
• SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding [68.94808536012371]
  We propose a tree-structured pre-trained conversation model, which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora. Our method can achieve new state-of-the-art results on the DialoGLUE benchmark consisting of seven datasets and four popular dialog understanding tasks.
  arXiv  Detail & Related papers  (2022-09-14T13:42:50Z)
• A Systematic Evaluation of Response Selection for Open Domain Dialogue [36.88551817451512]
  We curated a dataset where responses from multiple response generators produced for the same dialog context are manually annotated as appropriate (positive) and inappropriate (negative) We conduct a systematic evaluation of state-of-the-art methods for response selection, and demonstrate that both strategies of using multiple positive candidates and using manually verified hard negative candidates can bring in significant performance improvement in comparison to using the adversarial training data, e.g., increase of 3% and 13% in Recall@1 score, respectively.
  arXiv  Detail & Related papers  (2022-08-08T19:33:30Z)
• Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots [62.295373408415365]
  We propose a personalized hybrid matching network (PHMN) for context-response matching. Our contributions are two-fold: 1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information. We evaluate our model on two large datasets with user identification, i.e., personalized dialogue Corpus Ubuntu (P- Ubuntu) and personalized Weibo dataset (P-Weibo)
  arXiv  Detail & Related papers  (2021-03-17T09:42:11Z)
• A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation [5.56536459714557]
  Previous attempts to build effective semantics for Wizard-of-Oz (WOZ) conversations suffer from the difficulty in acquiring a high-quality, manually annotated training set. This paper proposes a new dialogue representation and a sample-efficient methodology that can predict precise dialogue states in WOZ conversations.
  arXiv  Detail & Related papers  (2020-09-16T22:52:46Z)
• A Large-Scale Chinese Short-Text Conversation Dataset [77.55813366932313]
  We present a large-scale cleaned Chinese conversation dataset, LCCC, which contains a base version (6.8million dialogues) and a large version (12.0 million dialogues) The quality of our dataset is ensured by a rigorous data cleaning pipeline, which is built based on a set of rules. We also release pre-training dialogue models which are trained on LCCC-base and LCCC-large respectively.
  arXiv  Detail & Related papers  (2020-08-10T08:12:49Z)
• Modality-Balanced Models for Visual Dialogue [102.35406085738325]
  The Visual Dialog task requires a model to exploit both image and conversational context information to generate the next response to the dialogue. We show that previous joint-modality (history and image) models over-rely on and are more prone to memorizing the dialogue history. We present methods for this integration of the two models, via ensemble and consensus dropout fusion with shared parameters.
  arXiv  Detail & Related papers  (2020-01-17T14:57:12Z)

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.