The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding

• URL: http://arxiv.org/abs/2209.07800v2
• Date: Fri, 26 May 2023 19:27:03 GMT
• Title: The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding
• Authors: Hao Fang, Anusha Balakrishnan, Harsh Jhamtani, John Bufe, Jean Crawford, Jayant Krishnamurthy, Adam Pauls, Jason Eisner, Jacob Andreas, Dan Klein
• Abstract summary: We describe a hybrid architecture for dialogue response generation that combines the strengths of neural language modeling and rule-based generation. Our experiments show that this system outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness.
• Score: 65.34601470417967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a real-world dialogue system, generated text must be truthful and informative while remaining fluent and adhering to a prescribed style. Satisfying these constraints simultaneously is difficult for the two predominant paradigms in language generation: neural language modeling and rule-based generation. We describe a hybrid architecture for dialogue response generation that combines the strengths of both paradigms. The first component of this architecture is a rule-based content selection model defined using a new formal framework called dataflow transduction, which uses declarative rules to transduce a dialogue agent's actions and their results (represented as dataflow graphs) into context-free grammars representing the space of contextually acceptable responses. The second component is a constrained decoding procedure that uses these grammars to constrain the output of a neural language model, which selects fluent utterances. Our experiments show that this system outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness.

Related papers

• Learning Phonotactics from Linguistic Informants [54.086544221761486]
  Our model iteratively selects or synthesizes a data-point according to one of a range of information-theoretic policies. We find that the information-theoretic policies that our model uses to select items to query the informant achieve sample efficiency comparable to, or greater than, fully supervised approaches.
  arXiv  Detail & Related papers  (2024-05-08T00:18:56Z)
• Text2Data: Low-Resource Data Generation with Textual Control [104.38011760992637]
  Natural language serves as a common and straightforward control signal for humans to interact seamlessly with machines. We propose Text2Data, a novel approach that utilizes unlabeled data to understand the underlying data distribution through an unsupervised diffusion model. It undergoes controllable finetuning via a novel constraint optimization-based learning objective that ensures controllability and effectively counteracts catastrophic forgetting.
  arXiv  Detail & Related papers  (2024-02-08T03:41:39Z)
• An xAI Approach for Data-to-Text Processing with ASP [39.58317527488534]
  This paper presents a framework that is compliant with xAI requirements. The text description is hierarchically organized, in a top-down structure where text is enriched with further details. The generation of natural language descriptions' structure is also managed by logic rules.
  arXiv  Detail & Related papers  (2023-08-30T09:09:09Z)
• Pre-training Multi-party Dialogue Models with Latent Discourse Inference [85.9683181507206]
  We pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model.
  arXiv  Detail & Related papers  (2023-05-24T14:06:27Z)
• MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation [102.20036684996248]
  We propose MURMUR, a neuro-symbolic modular approach to text generation from semi-structured data with multi-step reasoning. We conduct experiments on two data-to-text generation tasks like WebNLG and LogicNLG.
  arXiv  Detail & Related papers  (2022-12-16T17:36:23Z)
• Collocation2Text: Controllable Text Generation from Guide Phrases in Russian [0.0]
  Collocation2Text is a plug-and-play method for automatic controllable text generation in Russian. The method is based on two interacting models: the autoregressive language ruGPT-3 model and the autoencoding language ruRoBERTa model. Experiments on generating news articles using the proposed method showed its effectiveness for automatically generated fluent texts.
  arXiv  Detail & Related papers  (2022-06-18T17:10:08Z)
• An End-to-end Chinese Text Normalization Model based on Rule-guided Flat-Lattice Transformer [37.0774363352316]
  We propose an end-to-end Chinese text normalization model, which accepts Chinese characters as direct input. We also release a first publicly accessible largescale dataset for Chinese text normalization.
  arXiv  Detail & Related papers  (2022-03-31T11:19:53Z)
• Context Matters in Semantically Controlled Language Generation for Task-oriented Dialogue Systems [6.1478669848771546]
  This work combines information about the dialogue history encoded by pre-trained model with a meaning representation of the current system utterance to realize contextual language generation in task-oriented dialogues. We utilize the pre-trained multi-context ConveRT model for context representation in a model trained from scratch; and leverage the immediate preceding user utterance for context generation in a model adapted from the pre-trained GPT-2.
  arXiv  Detail & Related papers  (2021-11-28T11:48:02Z)
• A Controllable Model of Grounded Response Generation [122.7121624884747]
  Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process. We propose a framework that we call controllable grounded response generation (CGRG) We show that using this framework, a transformer based model with a novel inductive attention mechanism, trained on a conversation-like Reddit dataset, outperforms strong generation baselines.
  arXiv  Detail & Related papers  (2020-05-01T21:22:08Z)

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.