An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue Generation

• URL: http://arxiv.org/abs/2003.04195v1
• Date: Mon, 9 Mar 2020 15:20:21 GMT
• Title: An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue Generation
• Authors: Piji Li
• Abstract summary: We present an empirical investigation of pre-trained Transformer-based auto-regressive language models for the task of open-domain dialogue generation. Training paradigm of pre-training and fine-tuning is employed to conduct learning. Experiments are conducted on the typical single-turn and multi-turn dialogue corpora such as Weibo, Douban, Reddit, DailyDialog, and Persona-Chat.
• Score: 23.343006562849126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an empirical investigation of pre-trained Transformer-based auto-regressive language models for the task of open-domain dialogue generation. Training paradigm of pre-training and fine-tuning is employed to conduct the parameter learning. Corpora of News and Wikipedia in Chinese and English are collected for the pre-training stage respectively. Dialogue context and response are concatenated into a single sequence utilized as the input of the models during the fine-tuning stage. A weighted joint prediction paradigm for both context and response is designed to evaluate the performance of models with or without the loss term for context prediction. Various of decoding strategies such as greedy search, beam search, top-k sampling, etc. are employed to conduct the response text generation. Extensive experiments are conducted on the typical single-turn and multi-turn dialogue corpora such as Weibo, Douban, Reddit, DailyDialog, and Persona-Chat. Detailed numbers of automatic evaluation metrics on relevance and diversity of the generated results for the languages models as well as the baseline approaches are reported.

Related papers

• SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization [48.284512017469524]
  Multi-turn dialogues are characterized by their extended length and the presence of turn-taking conversations. Traditional language models often overlook the distinct features of these dialogues by treating them as regular text. We propose a speaker-enhanced pre-training method for long dialogue summarization.
  arXiv  Detail & Related papers  (2024-01-31T04:50:00Z)
• Promoting Open-domain Dialogue Generation through Learning Pattern Information between Contexts and Responses [5.936682548344234]
  This paper improves the quality of generated responses by learning the implicit pattern information between contexts and responses in the training samples. We also design a response-aware mechanism for mining the implicit pattern information between contexts and responses so that the generated replies are more diverse and approximate to human replies.
  arXiv  Detail & Related papers  (2023-09-06T08:11:39Z)
• Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling [70.23876429382969]
  We propose a benchmark that can evaluate intra-sentence discourse properties across a diverse set of NLP tasks. Disco-Bench consists of 9 document-level testsets in the literature domain, which contain rich discourse phenomena. For linguistic analysis, we also design a diagnostic test suite that can examine whether the target models learn discourse knowledge.
  arXiv  Detail & Related papers  (2023-07-16T15:18:25Z)
• Pre-training Multi-party Dialogue Models with Latent Discourse Inference [85.9683181507206]
  We pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model.
  arXiv  Detail & Related papers  (2023-05-24T14:06:27Z)
• Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking [57.92608483099916]
  Large pre-trained language models (PLMs) have shown impressive unaided performance across many NLP tasks. For more complex tasks such as dialogue state tracking (DST), designing prompts that reliably convey the desired intent is nontrivial. We introduce a saliency model to limit dialogue text length, allowing us to include more exemplars per query.
  arXiv  Detail & Related papers  (2023-02-12T15:05:10Z)
• 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)
• DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation [80.45816053153722]
  DialogVED introduces continuous latent variables into the enhanced encoder-decoder pre-training framework to increase the relevance and diversity of responses. We conduct experiments on PersonaChat, DailyDialog, and DSTC7-AVSD benchmarks for response generation.
  arXiv  Detail & Related papers  (2022-04-27T16:18:15Z)
• Towards Generalized Models for Task-oriented Dialogue Modeling on Spoken Conversations [22.894541507068933]
  This paper presents our approach to build generalized models for the Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations Challenge of DSTC-10. We employ extensive data augmentation strategies on written data, including artificial error injection and round-trip text-speech transformation. Our approach ranks third on the objective evaluation and second on the final official human evaluation.
  arXiv  Detail & Related papers  (2022-03-08T12:26:57Z)
• Context Matters in Semantically Controlled Language Generation for Task-oriented Dialogue Systems [6.1478669848771546]
  This work combines information about the dialogue history encoded by pre-trained model with a meaning representation of the current system utterance to realize contextual language generation in task-oriented dialogues. We utilize the pre-trained multi-context ConveRT model for context representation in a model trained from scratch; and leverage the immediate preceding user utterance for context generation in a model adapted from the pre-trained GPT-2.
  arXiv  Detail & Related papers  (2021-11-28T11:48:02Z)

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.