Pretrained Language Models for Dialogue Generation with Multiple Input Sources

• URL: http://arxiv.org/abs/2010.07576v1
• Date: Thu, 15 Oct 2020 07:53:28 GMT
• Title: Pretrained Language Models for Dialogue Generation with Multiple Input Sources
• Authors: Yu Cao, Wei Bi, Meng Fang, Dacheng Tao
• Abstract summary: In this work, we study dialogue models with multiple input sources adapted from the pretrained language model GPT2. We explore various methods to fuse multiple separate attention information corresponding to different sources. Our experimental results show that proper fusion methods deliver higher relevance with dialogue history than simple fusion baselines.
• Score: 101.17537614998805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks. However, it is still under investigating how to apply them to dialogue generation tasks, especially those with responses conditioned on multiple sources. Previous work simply concatenates all input sources or averages information from different input sources. In this work, we study dialogue models with multiple input sources adapted from the pretrained language model GPT2. We explore various methods to fuse multiple separate attention information corresponding to different sources. Our experimental results show that proper fusion methods deliver higher relevance with dialogue history than simple fusion baselines.

Related papers

• Citations as Queries: Source Attribution Using Language Models as Rerankers [2.3605348648054454]
  We conduct experiments on two datasets, English Wikipedia and medieval Arabic historical writing. We find that semisupervised methods can be nearly as effective as fully supervised methods.
  arXiv  Detail & Related papers  (2023-06-29T22:13:38Z)
• TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models [5.668457303716451]
  We propose TextMI as a general, competitive baseline for multimodal behavioral analysis tasks. Our approach significantly reduces model complexity, adds interpretability to the model's decision, and can be applied for a diverse set of tasks.
  arXiv  Detail & Related papers  (2023-03-27T17:54:32Z)
• Grounding Language Models to Images for Multimodal Inputs and Outputs [89.30027812161686]
  We propose an efficient method to ground pretrained text-only language models to the visual domain. We process arbitrarily interleaved image-and-text data, and generate text interleaved with retrieved images.
  arXiv  Detail & Related papers  (2023-01-31T18:33:44Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation [53.87485260058957]
  We study video-grounded dialogue generation, where a response is generated based on the dialogue context and the associated video. The primary challenges of this task lie in (1) the difficulty of integrating video data into pre-trained language models (PLMs) We propose a multi-agent reinforcement learning method to collaboratively perform reasoning on different modalities.
  arXiv  Detail & Related papers  (2022-10-22T14:45:29Z)
• A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation [107.82729587882397]
  It is expensive to scale up current persona-based dialogue datasets. Each data sample in this task is more complex to learn with than conventional dialogue data. We propose a data manipulation method, which is model-agnostic to be packed with any persona-based dialogue generation model.
  arXiv  Detail & Related papers  (2022-04-21T03:49:54Z)
• Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling [80.51094098799736]
  We propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder. BiDeN explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks. Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.
  arXiv  Detail & Related papers  (2022-04-18T03:51:46Z)
• Multimodal Dialogue Response Generation [27.611204319057393]
  We present a multimodal dialogue generation model, which takes the dialogue history as input, then generates a textual sequence or an image as response. We consider multimodal dialogue generation under a natural assumption that only limited training examples are available. In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire model.
  arXiv  Detail & Related papers  (2021-10-16T08:52:26Z)
• Multi-Referenced Training for Dialogue Response Generation [36.24321477524634]
  We show that gap between the real world probability distribution and the single-referenced data's probability distribution prevents the model from learning the one-to-many relations efficiently. We generate diverse pseudo references from a powerful pretrained model to build multi-referenced data that provides a better approximation of the real-world distribution.
  arXiv  Detail & Related papers  (2020-09-15T14:17:53Z)
• An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue Generation [23.343006562849126]
  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.
  arXiv  Detail & Related papers  (2020-03-09T15:20:21Z)

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.