Meta-Context Transformers for Domain-Specific Response Generation

• URL: http://arxiv.org/abs/2010.05572v1
• Date: Mon, 12 Oct 2020 09:49:27 GMT
• Title: Meta-Context Transformers for Domain-Specific Response Generation
• Authors: Debanjana Kar, Suranjana Samanta, Amar Prakash Azad
• Abstract summary: We present DSRNet, a transformer-based model for dialogue response generation by reinforcing domain-specific attributes. We study the use of DSRNet in a multi-turn multi-interlocutor environment for domain-specific response generation. Our model shows significant improvement over the state-of-the-art for multi-turn dialogue systems supported by better BLEU and semantic similarity (BertScore) scores.
• Score: 4.377737808397113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the tremendous success of neural dialogue models in recent years, it suffers a lack of relevance, diversity, and some times coherence in generated responses. Lately, transformer-based models, such as GPT-2, have revolutionized the landscape of dialogue generation by capturing the long-range structures through language modeling. Though these models have exhibited excellent language coherence, they often lack relevance and terms when used for domain-specific response generation. In this paper, we present DSRNet (Domain Specific Response Network), a transformer-based model for dialogue response generation by reinforcing domain-specific attributes. In particular, we extract meta attributes from context and infuse them with the context utterances for better attention over domain-specific key terms and relevance. We study the use of DSRNet in a multi-turn multi-interlocutor environment for domain-specific response generation. In our experiments, we evaluate DSRNet on Ubuntu dialogue datasets, which are mainly composed of various technical domain related dialogues for IT domain issue resolutions and also on CamRest676 dataset, which contains restaurant domain conversations. Trained with maximum likelihood objective, our model shows significant improvement over the state-of-the-art for multi-turn dialogue systems supported by better BLEU and semantic similarity (BertScore) scores. Besides, we also observe that the responses produced by our model carry higher relevance due to the presence of domain-specific key attributes that exhibit better overlap with the attributes of the context. Our analysis shows that the performance improvement is mostly due to the infusion of key terms along with dialogues which result in better attention over domain-relevant terms. Other contributing factors include joint modeling of dialogue context with the domain-specific meta attributes and topics.

Related papers

• PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment [58.46761798403072]
  A model-based automatic dialogue evaluation metric (ADEM) is expected to perform well across multiple domains. Despite significant progress, an ADEM that works well in one domain does not necessarily generalize to another. We propose a Panel of Experts (PoE) network that consists of a shared transformer encoder and a collection of lightweight adapters.
  arXiv  Detail & Related papers  (2022-12-18T02:26:50Z)
• Dialogue Term Extraction using Transfer Learning and Topological Data Analysis [0.8185867455104834]
  We explore different features that can enable systems to discover realizations of domains, slots, and values in dialogues in a purely data-driven fashion. To examine the utility of each feature set, we train a seed model based on the widely used MultiWOZ data-set. Our method outperforms the previously proposed approach that relies solely on word embeddings.
  arXiv  Detail & Related papers  (2022-08-22T17:04:04Z)
• Improving Multi-Party Dialogue Discourse Parsing via Domain Integration [25.805553277418813]
  Multi-party conversations are implicitly organized by semantic level correlations across the interactive turns. dialogue discourse analysis can be applied to predict the dependency structure and relations between the elementary discourse units. Existing corpora with dialogue discourse annotation are collected from specific domains with limited sample sizes.
  arXiv  Detail & Related papers  (2021-10-09T09:36:22Z)
• 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)
• Cross Copy Network for Dialogue Generation [44.593899479668416]
  We propose a novel network architecture - Cross Copy Networks(CCN) to explore the current dialog context and similar dialogue instances' logical structure simultaneously. Experiments with two tasks, court debate and customer service content generation, proved that the proposed algorithm is superior to existing state-of-art content generation models.
  arXiv  Detail & Related papers  (2020-10-22T09:03:23Z)
• RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling [35.75880078666584]
  RiSAWOZ is a large-scale multi-domain Chinese Wizard-of-Oz dataset with Rich Semantic s. It contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialogues, with more than 150K utterances spanning over 12 domains.
  arXiv  Detail & Related papers  (2020-10-17T08:18:59Z)
• Meta Dialogue Policy Learning [58.045067703675095]
  We propose Deep Transferable Q-Network (DTQN) to utilize shareable low-level signals between domains. We decompose the state and action representation space into feature subspaces corresponding to these low-level components. In experiments, our model outperforms baseline models in terms of both success rate and dialogue efficiency.
  arXiv  Detail & Related papers  (2020-06-03T23:53:06Z)
• UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues [101.96097419995556]
  "UniConv" is a novel unified neural architecture for end-to-end conversational systems in task-oriented dialogues. We conduct comprehensive experiments in dialogue state tracking, context-to-text, and end-to-end settings on the MultiWOZ2.1 benchmark.
  arXiv  Detail & Related papers  (2020-04-29T16:28:22Z)
• Non-Autoregressive Dialog State Tracking [122.2328875457225]
  We propose a novel framework of Non-Autoregressive Dialog State Tracking (NADST) NADST can factor in potential dependencies among domains and slots to optimize the models towards better prediction of dialogue states as a complete set rather than separate slots. Our results show that our model achieves the state-of-the-art joint accuracy across all domains on the MultiWOZ 2.1 corpus.
  arXiv  Detail & Related papers  (2020-02-19T06:39:26Z)
• MA-DST: Multi-Attention Based Scalable Dialog State Tracking [13.358314140896937]
  Dialog State Tracking dialog agents provide a natural language interface for users to complete their goal. To enable accurate multi-domain DST, the model needs to encode dependencies between past utterances and slot semantics. We introduce a novel architecture for this task to encode the conversation history and slot semantics.
  arXiv  Detail & Related papers  (2020-02-07T05:34:58Z)
• Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation [59.174903564894954]
  In this work, we extend this approach to the task of dialog state tracking for goal-oriented dialogs. We propose the Variational Hierarchical Dialog Autoencoder (VHDA) for modeling the complete aspects of goal-oriented dialogs. Experiments on various dialog datasets show that our model improves the downstream dialog trackers' robustness via generative data augmentation.
  arXiv  Detail & Related papers  (2020-01-23T15:34:56Z)

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.