Papers with response generation models
Lexical Entrainment for Conversational Systems (2023.findings-emnlp)
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| Challenge: | Conversational agents are expected to possess human-like features such as lexical entrainment (LE). |
| Approach: | They propose a dataset and a measure for LE for conversational systems to explicitly integrate LE into conversational system. |
| Outcome: | The proposed dataset and a measure for LE for conversational systems address this human-like phenomenon. |
Learning to Control the Specificity in Neural Response Generation (P18-1)
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| Challenge: | Existing generative conversational models tend to favor general and trivial responses which appear frequently. |
| Approach: | They propose a controlled response generation mechanism to handle different utterance-response relationships in terms of specificity. |
| Outcome: | The proposed model outperforms state-of-the-art models under automatic and human evaluations. |
Low-Resource Response Generation with Template Prior (D19-1)
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| Challenge: | Existing open domain response generation models are limited to paired data, but are less explored in real-world applications. |
| Approach: | They propose to train a neural response generation model with unpaired data and paired data as prior. |
| Outcome: | The proposed model outperforms state-of-the-art models in both automatic and human evaluation when only a few pairs are available. |
The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents (2023.eacl-main)
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| Challenge: | StatCan Dialogue Dataset consists of 19,379 conversation turns between agents and online users . researchers propose two tasks to help knowledge workers find relevant tables for live chat users based on real-world intents . |
| Approach: | They propose two tasks based on 19,379 conversation turns between agents and online users . they investigate the difficulty of each task by establishing strong baselines . |
| Outcome: | The proposed task is based on a dataset of 19,379 conversation turns . the researchers show that the models struggle to generalize to future conversations . |
Well Begun is Half Done: Generator-agnostic Knowledge Pre-Selection for Knowledge-Grounded Dialogue (2023.emnlp-main)
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| Challenge: | Existing knowledge selection methods are costly to learn and difficult to interpret when errors arise in the generated responses. |
| Approach: | They propose a generator-agnostic knowledge selection method to select context-related knowledge among different knowledge structures and variable knowledge requirements. |
| Outcome: | The proposed method can select knowledge accurately in advance and reduce learning, adjustment, and interpretation burden of later models. |
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations (2022.acl-long)
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| Challenge: | Experimental results show that HeterMPC outperforms various baseline models for response generation in multi-party conversations. |
| Approach: | They propose a heterogeneous graph-based neural network for response generation in multi-party conversations which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph. |
| Outcome: | The proposed model outperforms baseline models on the Ubuntu Internet Relay Chat (IRC) channel. |
Interactive Evaluation of Dialog Track at DSTC9 (2022.lrec-1)
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| Challenge: | Currently, dialog research is focused on static data, which neglects multiple important properties of dialog, such as consistency, topic depth, adaptation, error recovery and user-centric development. |
| Approach: | They propose to use static dialogs to build strong response generation models and extend them to back-and-forth interactions with real users. |
| Outcome: | The proposed model trains a larger evolved Transformer model on social media data and attains strong performance in interactive settings. |
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection (2020.emnlp-main)
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| Challenge: | Existing approaches to learning-to-rank response selection are suboptimal due to ignorance of diversity of response quality. |
| Approach: | They propose to use off-the-shelf response retrieval models as automatic grayscale data generators to train response selection models. |
| Outcome: | The proposed approach can be automated without human effort on grayscale data. |
A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems (2024.findings-acl)
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| Challenge: | Recent large-scale neural response generation models (RGMs) have made significant progress but still struggle to generate semantically appropriate responses. |
| Approach: | They build a large dataset of model-generated contradictions for the first time and analyze the results to gain valuable insights into their characteristics. |
| Outcome: | The proposed dataset significantly improves the performance of data-driven contradiction suppression methods. |