Adapting Task-Oriented Dialogue Models for Email Conversations

• URL: http://arxiv.org/abs/2208.09439v1
• Date: Fri, 19 Aug 2022 16:41:34 GMT
• Title: Adapting Task-Oriented Dialogue Models for Email Conversations
• Authors: Soham Deshmukh, Charles Lee
• Abstract summary: In this paper, we provide an effective transfer learning framework (EMToD) that allows the latest development in dialogue models to be adapted for long-form conversations. We show that the proposed EMToD framework improves intent detection performance over pre-trained language models by 45% and over pre-trained dialogue models by 30% for task-oriented email conversations.
• Score: 4.45709593827781
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intent detection is a key part of any Natural Language Understanding (NLU) system of a conversational assistant. Detecting the correct intent is essential yet difficult for email conversations where multiple directives and intents are present. In such settings, conversation context can become a key disambiguating factor for detecting the user's request from the assistant. One prominent way of incorporating context is modeling past conversation history like task-oriented dialogue models. However, the nature of email conversations (long form) restricts direct usage of the latest advances in task-oriented dialogue models. So in this paper, we provide an effective transfer learning framework (EMToD) that allows the latest development in dialogue models to be adapted for long-form conversations. We show that the proposed EMToD framework improves intent detection performance over pre-trained language models by 45% and over pre-trained dialogue models by 30% for task-oriented email conversations. Additionally, the modular nature of the proposed framework allows plug-and-play for any future developments in both pre-trained language and task-oriented dialogue models.

Related papers

• SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents [72.42049370297849]
  SpokenWOZ is a large-scale speech-text dataset for spoken TOD. Cross-turn slot and reasoning slot detection are new challenges for SpokenWOZ.
  arXiv  Detail & Related papers  (2023-05-22T13:47:51Z)
• Joint Modelling of Spoken Language Understanding Tasks with Integrated Dialog History [30.20353302347147]
  We propose a novel model architecture that learns dialog context to jointly predict the intent, dialog act, speaker role, and emotion for the spoken utterance. Our experiments show that our joint model achieves similar results to task-specific classifiers.
  arXiv  Detail & Related papers  (2023-05-01T16:26:18Z)
• SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation [123.37377363355363]
  SPACE-3 is a novel unified semi-supervised pre-trained conversation model learning from large-scale dialog corpora. It can be effectively fine-tuned on a wide range of downstream dialog tasks. Results show that SPACE-3 achieves state-of-the-art performance on eight downstream dialog benchmarks.
  arXiv  Detail & Related papers  (2022-09-14T14:17:57Z)
• GODEL: Large-Scale Pre-Training for Goal-Directed Dialog [119.1397031992088]
  We introduce GODEL, a large pre-trained language model for dialog. We show that GODEL outperforms state-of-the-art pre-trained dialog models in few-shot fine-tuning setups. A novel feature of our evaluation methodology is the introduction of a notion of utility that assesses the usefulness of responses.
  arXiv  Detail & Related papers  (2022-06-22T18:19:32Z)
• Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning [27.92734269206744]
  InstructDial is an instruction tuning framework for dialogue. It consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets. Our analysis reveals that InstructDial enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection, and even better performance in a few-shot setting.
  arXiv  Detail & Related papers  (2022-05-25T11:37:06Z)
• KETOD: Knowledge-Enriched Task-Oriented Dialogue [77.59814785157877]
  Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. We investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model.
  arXiv  Detail & Related papers  (2022-05-11T16:01:03Z)
• Response Generation with Context-Aware Prompt Learning [19.340498579331555]
  We present a novel approach for pre-trained dialogue modeling that casts the dialogue generation problem as a prompt-learning task. Instead of fine-tuning on limited dialogue data, our approach, DialogPrompt, learns continuous prompt embeddings optimized for dialogue contexts. Our approach significantly outperforms the fine-tuning baseline and the generic prompt-learning methods.
  arXiv  Detail & Related papers  (2021-11-04T05:40:13Z)
• "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)
• Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems [21.98135285833616]
  Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. We present a new approach for building goal-oriented dialogue systems that is scalable, as well as data efficient.
  arXiv  Detail & Related papers  (2021-04-19T07:09:27Z)
• TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue [113.45485470103762]
  In this work, we unify nine human-human and multi-turn task-oriented dialogue datasets for language modeling. To better model dialogue behavior during pre-training, we incorporate user and system tokens into the masked language modeling.
  arXiv  Detail & Related papers  (2020-04-15T04:09:05Z)

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.