DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation

• URL: http://arxiv.org/abs/2102.02191v1
• Date: Wed, 3 Feb 2021 18:36:58 GMT
• Title: DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation
• Authors: Sarik Ghazarian, Zixi Liu, Tuhin Chakrabarty, Xuezhe Ma, Aram Galstyan, and Nanyun Peng
• Abstract summary: RecentoL is an open-domain dialogue system that leverages conversational lines as controllable and content-planning elements to guide the generation model. Two primary modules inoL's pipeline are conditional generators trained for 1) predicting relevant and informative convlines for dialogue contexts and 2) generating high-quality responses conditioned on the predicted convlines.
• Score: 33.53084158275457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Having engaging and informative conversations with users is the utmost goal for open-domain conversational systems. Recent advances in transformer-based language models and their applications to dialogue systems have succeeded to generate fluent and human-like responses. However, they still lack control over the generation process towards producing contentful responses and achieving engaging conversations. To achieve this goal, we present \textbf{DiSCoL} (\textbf{Di}alogue \textbf{S}ystems through \textbf{Co}versational \textbf{L}ine guided response generation). DiSCoL is an open-domain dialogue system that leverages conversational lines (briefly \textbf{convlines}) as controllable and informative content-planning elements to guide the generation model produce engaging and informative responses. Two primary modules in DiSCoL's pipeline are conditional generators trained for 1) predicting relevant and informative convlines for dialogue contexts and 2) generating high-quality responses conditioned on the predicted convlines. Users can also change the returned convlines to \textit{control} the direction of the conversations towards topics that are more interesting for them. Through automatic and human evaluations, we demonstrate the efficiency of the convlines in producing engaging conversations.

Related papers

• OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation [53.7173034249361]
  End-to-end GPT-based model OmniFlatten capable of effectively modeling complex behaviors inherent natural conversations with low latency. Our approach offers a simple modeling technique and a promising research direction for developing efficient and natural end-to-end full- spoken dialogue systems.
  arXiv  Detail & Related papers  (2024-10-23T11:58:58Z)
• Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs [59.74002011562726]
  We propose a novel linguistic cue-based chain-of-thoughts (textitCue-CoT) to provide a more personalized and engaging response. We build a benchmark with in-depth dialogue questions, consisting of 6 datasets in both Chinese and English. Empirical results demonstrate our proposed textitCue-CoT method outperforms standard prompting methods in terms of both textithelpfulness and textitacceptability on all datasets.
  arXiv  Detail & Related papers  (2023-05-19T16:27:43Z)
• Controllable Mixed-Initiative Dialogue Generation through Prompting [50.03458333265885]
  Mixed-initiative dialogue tasks involve repeated exchanges of information and conversational control. Agents gain control by generating responses that follow particular dialogue intents or strategies, prescribed by a policy planner. Standard approach has been fine-tuning pre-trained language models to perform generation conditioned on these intents. We instead prompt large language models as a drop-in replacement to fine-tuning on conditional generation.
  arXiv  Detail & Related papers  (2023-05-06T23:11:25Z)
• Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems [8.419582942080927]
  Response generation is one of the critical components in task-oriented dialog systems. We propose an approach that performs textit dynamic prompting where the prompts are learnt from dialog contexts. We show that contextual dynamic prompts improve response generation in terms of textit combined score citemehri-etal 2019-structured by 3 absolute points.
  arXiv  Detail & Related papers  (2023-01-30T20:26:02Z)
• FCTalker: Fine and Coarse Grained Context Modeling for Expressive Conversational Speech Synthesis [75.74906149219817]
  Conversational Text-to-Speech (TTS) aims to synthesis an utterance with the right linguistic and affective prosody in a conversational context. We propose a novel expressive conversational TTS model, as termed FCTalker, that learn the fine and coarse grained context dependency at the same time during speech generation.
  arXiv  Detail & Related papers  (2022-10-27T12:20:20Z)
• Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation [32.764356638437214]
  We introduce a new technique for target-guided response generation. We also propose techniques to re-purpose existing dialogue datasets for target-guided generation. Our work generally enables dialogue system designers to exercise more control over the conversations that their systems produce.
  arXiv  Detail & Related papers  (2022-05-19T04:01:40Z)
• An Approach to Inference-Driven Dialogue Management within a Social Chatbot [10.760026478889667]
  Instead of framing conversation as a sequence of response generation tasks, we model conversation as a collaborative inference process. Our pipeline accomplishes this modelling in three broad stages. This approach lends itself to understanding latent semantics of user inputs, flexible initiative taking, and responses that are novel and coherent with the dialogue context.
  arXiv  Detail & Related papers  (2021-10-31T19:01:07Z)
• Fusing task-oriented and open-domain dialogues in conversational agents [12.338220374261343]
  Two dialogue modes can potentially be intertwined together seamlessly in the same conversation, as easily done by a friendly human assistant. Our paper addresses this problem of fusing TODs and ODDs in multi-turn dialogues. It features inter-mode contextual dependency, i.e., the dialogue turns from the two modes depend on each other.
  arXiv  Detail & Related papers  (2021-09-09T09:48:26Z)
• Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries [3.593955557310285]
  Most dialogue systems rely on hybrid approaches for generating a set of ranked responses. We design a neural approach which generates responses which are contextually aware with the user query. Our simple approach makes use of rules over dependency parses and a text-to-text transformer fine-tuned on synthetic data of question-response pairs.
  arXiv  Detail & Related papers  (2020-12-03T12:34:22Z)
• Towards Data Distillation for End-to-end Spoken Conversational Question Answering [65.124088336738]
  We propose a new Spoken Conversational Question Answering task (SCQA) SCQA aims at enabling QA systems to model complex dialogues flow given the speech utterances and text corpora. Our main objective is to build a QA system to deal with conversational questions both in spoken and text forms.
  arXiv  Detail & Related papers  (2020-10-18T05:53:39Z)
• Controlling Dialogue Generation with Semantic Exemplars [55.460082747572734]
  We present an Exemplar-based Dialogue Generation model, EDGE, that uses the semantic frames present in exemplar responses to guide generation. We show that controlling dialogue generation based on the semantic frames of exemplars, rather than words in the exemplar itself, improves the coherence of generated responses.
  arXiv  Detail & Related papers  (2020-08-20T17:02:37Z)

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.