Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method (D18-1)
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| Challenge: | Experimental results show that Sequence-to-sequence models tend to generate generic/dull responses . |
| Approach: | They propose a statistical re-weighting method that assigns different weights for multiple responses of the same query. |
| Outcome: | The proposed method improves acceptance rate of generated responses and significantly reduces generated generic responses. |
Similar Papers
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints (D18-1)
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| Challenge: | Neural conversation models tend to generate safe, generic responses for most inputs . this is due to the limitations of likelihood-based decoding objectives in generation tasks with diverse outputs, such as conversation. |
| Approach: | They propose a distributional constraint approach that incorporates side information into the generated responses. |
| Outcome: | The proposed approach generates responses that are less generic without sacrificing plausibility. |
Improving Neural Conversational Models with Entropy-Based Data Filtering (P19-1)
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| Challenge: | Current neural network-based conversational models lack diversity and generate boring responses to open-ended utterances. |
| Approach: | They propose an unsupervised method of filtering dialog datasets by removing generic utterances from training data using an entropy-based approach that does not require human supervision. |
| Outcome: | The proposed method improves dialog quality as chatbots learn to output more diverse responses to open-ended utterances. |
Adaptive Parameterization for Neural Dialogue Generation (D19-1)
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| Challenge: | Existing models of open-domain dialogue generate responses based on sequence-to-sequence paradigms. |
| Approach: | They propose an Adaptive Neural Dialogue generation model which manages various conversations with conversation-specific parameterization. |
| Outcome: | The proposed model performs better on a large-scale conversational dataset. |
Elastic Weight Removal for Faithful and Abstractive Dialogue Generation (2024.naacl-long)
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| Challenge: | Current-day large language models generate coherent, grammatical, and seemingly meaningful text, but are prone to hallucinating incorrect information. |
| Approach: | They propose to ‘subtract’ parameters of a model trained to hallucinate from a dialogue response generation model to ‘negate’ the contribution of such hallucinatedexamples from it. |
| Outcome: | The proposed method reduces hallucinations and discourages extractive responses, which are often a consequence of reducing hallucines by encouraging copy-pasting of document spans. |
Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References (P19-1)
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| Challenge: | Existing approaches to open-domain dialogue generation ignore the nature of 1-to-1 mapping that there may exist multiple valid responses corresponding to the same query. |
| Approach: | They propose to model open-domain dialogue generation using 1-to-1 mapping . they first extract common features of different responses and then combine them with distinctive features to generate multiple diverse and appropriate responses. |
| Outcome: | The proposed model outperforms existing models on automatic and human evaluations. |
Automatic Generation of Large-scale Multi-turn Dialogues from Reddit (2022.coling-1)
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| Challenge: | Using a set of algorithms, we can generate large dialogue corpus from Reddit. |
| Approach: | They propose to automatically convert posts and their comments from discussion forums such as Reddit into multi-turn dialogues. |
| Outcome: | The proposed methods improve on the baseline method by 36.3% . the best method shows an improvement of 36.6% over the previous one . |
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020.coling-industry)
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Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White
| Challenge: | Natural language generation (NLG) is a critical component in conversational systems . Traditionally, NLG components have been deployed using template-based solutions . however, deployment of such model-based systems has been challenging due to high latency and data needs. |
| Approach: | They propose a family of techniques to deploy data-efficient neural solutions for NLG in conversational systems to production. |
| Outcome: | The proposed techniques achieve production quality with light-weight neural network models using fraction of the data needed otherwise. |
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight (2020.acl-main)
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| Challenge: | Current state-of-the-art neural dialogue models learn from human conversations . however, due to the open-ended nature of human conversations, the quality of training data varies . |
| Approach: | They propose a data manipulation framework to augment and highlight effective training samples . they also propose to increase its manipulation skills through gradient descent with validation samples a reshaping framework to proactively restructure the data distribution towards reliable samples is also proposed . |
| Outcome: | The proposed framework improves the performance of open-domain neural dialogue models with respect to evaluation metrics and human judgments. |
Fast and Light-Weight Answer Text Retrieval in Dialogue Systems (2022.naacl-industry)
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| Challenge: | a recent study shows that text retrieval can be used to find information relevant to user requests. |
| Approach: | They propose to use a corpus of text to search for relevant responses to user requests . they compare this approach to other methods that use intent detection . |
| Outcome: | a new approach can be used to search through a corpus of text to find relevant responses to user requests. |
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling (2020.coling-main)
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| Challenge: | Existing models for machine translation and dialogue response generation require a large number of handcrafted features. |
| Approach: | They propose to interpret a general neural model comparatively by using the seq2seq model in two mainstream NLP tasks. |
| Outcome: | The proposed model is used in two mainstream NLP tasks and is compared with a standard model. |