Challenge: Existing dialogue systems do not exploit document knowledge effectively enough.
Approach: They propose a Transformer-based architecture for document grounded conversations that incorporates document knowledge into a two-pass decoder to improve context coherence and knowledge correctness.
Outcome: The proposed model outperforms baselines on context coherence and knowledge relevance on a real-world document grounded dataset.

Similar Papers

A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation (2021.emnlp-main)

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Challenge: Existing knowledge-grounded dialogues perform poorly when transfer into new domains with limited training samples.
Approach: They propose a weakly supervised three-stage learning framework based on weakly-supervised learning based upon large scale ungrounded dialogues and unstructured knowledge base.
Outcome: The proposed framework outperforms state-of-the-art methods even in zero-resource setting.
A Dataset for Document Grounded Conversations (D18-1)

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Challenge: a dataset of document grounded conversations provides information on content of a document . current datasets lacking conversation grounding do not provide this information .
Approach: They propose a document grounded dataset for conversations . they use Wikipedia articles about popular movies to define document grounded conversations based on their results .
Outcome: The proposed dataset provides a source of information and provides benchmark performance on the task of generating the next response.
A Compare Aggregate Transformer for Understanding Document-grounded Dialogue (2020.findings-emnlp)

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Challenge: Existing studies have focused on KS in unstructured documents, but dialogue history that is not related to the current dialogue may introduce noise in the KS processing.
Approach: They propose a Compare Aggregate Transformer to jointly denoise the dialogue context and aggregate the document information for response generation.
Outcome: The proposed model outperforms the state-of-the-art approach and strong baselines on a CMU_DoG dataset.
Retrieval Augmentation Reduces Hallucination in Conversation (2021.findings-emnlp)

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Challenge: State-of-the-art dialogue models suffer from factual incorrectness and hallucination of knowledge.
Approach: They propose to use neural-retrieval-in-the-loop architectures to optimize knowledge-grounded dialogue by retrieving, ranking, and encoder-decoders.
Outcome: The proposed architectures exhibit open-domain conversational capabilities and generalize effectively to scenarios not within the training data.
Focused Attention Improves Document-Grounded Generation (2021.naacl-main)

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Challenge: Document grounded generation is the task of using the information provided in a document to improve text generation.
Approach: They propose two new document grounded generation tasks that use information provided in a document to improve text generation.
Outcome: The proposed models outperform existing methods on automated and human evaluation for closeness to reference and relevance to the document.
Improving the Transformer Translation Model with Document-Level Context (D18-1)

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Challenge: Existing models for document-level context translation ignore documentlevel context.
Approach: They propose a document-level context encoder to represent document- level context and integrate it into the Transformer model.
Outcome: Experiments on NIST Chinese-English and IWSLT French-English datasets show that the proposed translation model outperforms the Transformer model significantly.
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (2020.coling-main)

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Challenge: Existing models for ERTC use a few non-neutral categories to identify the emotion of each utterance.
Approach: They propose a novel Knowledge Aware Incremental Transformer with Multi-task Learning to address these challenges by leveraging commonsense knowledge to leverage context.
Outcome: The proposed model outperforms state-of-the-art models across five benchmark datasets.
Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems (2021.emnlp-main)

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Challenge: Existing approaches to integrate knowledge bases into end-to-end task-oriented dialogue systems are limited in their ability to properly represent the entity of KB.
Approach: They propose a framework that dynamically perceives all relevant entities and dialogue history . it uses a Memory Mask to enforce the entity to focus on its relevant entities .
Outcome: The proposed framework can achieve superior performance over the state of the arts.
RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation (2023.acl-long)

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Challenge: Existing approaches to personalized dialogue generation rely on dialogue data paired with user traits, profiles or persona description sentences.
Approach: They propose a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively.
Outcome: The proposed model generates more fluent and personalized responses under a suite of human and automatic metrics and is superior to state-of-the-art baselines on English Reddit conversations.
RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark (2026.findings-acl)

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Challenge: Existing benchmarks treat multi-turn conversation and reasoning-intensive retrieval separately, yet real-world information seeking requires both.
Approach: They propose a framework that transforms complex queries into fact-grounded multi-turn dialogues through multi-level validation.
Outcome: The proposed framework outperforms existing systems in a number of domains and can be used to improve multi-turn conversation retrieval.

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