Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks

• URL: http://arxiv.org/abs/2004.01972v2
• Date: Mon, 9 Nov 2020 07:11:33 GMT
• Title: Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks
• Authors: Yufan Zhao, Can Xu, Wei Wu, Lei Yu
• Abstract summary: We study multi-turn response generation for open-domain dialogues. In this work, we pursue a model that has a simple structure yet can effectively leverage conversation contexts for response generation.
• Score: 22.585901751927995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study multi-turn response generation for open-domain dialogues. The existing state-of-the-art addresses the problem with deep neural architectures. While these models improved response quality, their complexity also hinders the application of the models in real systems. In this work, we pursue a model that has a simple structure yet can effectively leverage conversation contexts for response generation. To this end, we propose four auxiliary tasks including word order recovery, utterance order recovery, masked word recovery, and masked utterance recovery, and optimize the objectives of these tasks together with maximizing the likelihood of generation. By this means, the auxiliary tasks that relate to context understanding can guide the learning of the generation model to achieve a better local optimum. Empirical studies with three benchmarks indicate that our model can significantly outperform state-of-the-art generation models in terms of response quality on both automatic evaluation and human judgment, and at the same time enjoys a much faster decoding process.

Related papers

• Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization [0.6629765271909505]
  This paper introduces a novel approach to model alignment through weak-to-strong generalization in the context of language models. Our results suggest that this facilitation-based approach not only enhances model performance but also provides insights into the nature of model alignment.
  arXiv  Detail & Related papers  (2024-09-11T15:16:25Z)
• RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback [19.28222902440827]
  Large language models (LLMs) demonstrate exceptional performance in numerous tasks but still heavily rely on knowledge stored in their parameters. Retrieval-augmented generation (RAG) methods address this issue by integrating external knowledge. We propose Retrieval Augmented Iterative Self-Feedback (RA-ISF), a framework that iteratively decomposes tasks and processes them in three submodules to enhance the model's problem-solving capabilities.
  arXiv  Detail & Related papers  (2024-03-11T16:01:05Z)
• Self-Convinced Prompting: Few-Shot Question Answering with Repeated Introspection [13.608076739368949]
  We introduce a novel framework that harnesses the potential of large-scale pre-trained language models. Our framework processes the output of a typical few-shot chain-of-thought prompt, assesses the correctness of the response, scrutinizes the answer, and ultimately produces a new solution.
  arXiv  Detail & Related papers  (2023-10-08T06:36:26Z)
• RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models [57.12888828853409]
  RAVEN is a model that combines retrieval-augmented masked language modeling and prefix language modeling. Fusion-in-Context Learning enables the model to leverage more in-context examples without requiring additional training. Our work underscores the potential of retrieval-augmented encoder-decoder language models for in-context learning.
  arXiv  Detail & Related papers  (2023-08-15T17:59:18Z)
• Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy [164.83371924650294]
  We show that strong performance can be achieved by a method we call Iter-RetGen, which synergizes retrieval and generation in an iterative manner. A model output shows what might be needed to finish a task, and thus provides an informative context for retrieving more relevant knowledge. Iter-RetGen processes all retrieved knowledge as a whole and largely preserves the flexibility in generation without structural constraints.
  arXiv  Detail & Related papers  (2023-05-24T16:17:36Z)
• SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation [54.66399120084227]
  Language models trained on large-scale corpora can generate remarkably fluent results in open-domain dialogue. For the persona-based dialogue generation task, consistency and coherence are great challenges for language models. A two-stage SimOAP strategy is proposed, i.e., over-sampling and post-evaluation.
  arXiv  Detail & Related papers  (2023-05-18T17:23:00Z)
• Improving Non-autoregressive Generation with Mixup Training [51.61038444990301]
  We present a non-autoregressive generation model based on pre-trained transformer models. We propose a simple and effective iterative training method called MIx Source and pseudo Target. Our experiments on three generation benchmarks including question generation, summarization and paraphrase generation, show that the proposed framework achieves the new state-of-the-art results.
  arXiv  Detail & Related papers  (2021-10-21T13:04:21Z)
• Enhancing Dialogue Generation via Multi-Level Contrastive Learning [57.005432249952406]
  We propose a multi-level contrastive learning paradigm to model the fine-grained quality of the responses with respect to the query. A Rank-aware (RC) network is designed to construct the multi-level contrastive optimization objectives. We build a Knowledge Inference (KI) component to capture the keyword knowledge from the reference during training and exploit such information to encourage the generation of informative words.
  arXiv  Detail & Related papers  (2020-09-19T02:41:04Z)
• Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues [88.73739515457116]
  We introduce four self-supervised tasks including next session prediction, utterance restoration, incoherence detection and consistency discrimination. We jointly train the PLM-based response selection model with these auxiliary tasks in a multi-task manner. Experiment results indicate that the proposed auxiliary self-supervised tasks bring significant improvement for multi-turn response selection.
  arXiv  Detail & Related papers  (2020-09-14T08:44:46Z)

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.