| Challenge: | Existing methods to learn to predict responses to messages are based on post-hoc diversification rather than learning to predict sets of responses. |
| Approach: | They propose a method that employs model-based simulation to discover high-value response sets by simulating possible user responses with a learned world model. |
| Outcome: | Empirically, the proposed method improves ROUGE score and Self-ROUGE scores on two public datasets compared to baselines. |
Similar Papers
End-to-End Autoregressive Retrieval via Bootstrapping for Smart Reply Systems (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Reply suggestion systems are poorly suited for out-of-the-box retrieval architectures, which only consider individual message-reply similarity. |
| Approach: | They propose an autoregressive text-to-text retrieval model that learns the smart reply task end-to end from a dataset of (message, reply set) pairs obtained via bootstrapping. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three datasets and shows that it is more diverse and relevant to the user. |
Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models lack specific task alignment and large-scale simulations are challenging due to their ambiguity, noise and massive volume. |
| Approach: | They propose a framework that leverages user feedback in RSs with advanced LLM capabilities to generate high-quality simulation data. |
| Outcome: | The proposed framework boosts the alignment with human preferences and in-domain reasoning capabilities of the fine-tuned LLMs. |
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
| Outcome: | The proposed model outperforms state-of-the-art methods in evaluation and human judgment. |
Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have focused on improving dialogue generation models that include knowledge related to the posts. |
| Approach: | They propose to use a novel method to generate responses from posts and related knowledge by injecting knowledge into dialogue generation models. |
| Outcome: | The proposed method outperforms baseline models in terms of knowledge relevance and quality. |
Answer Generation for Retrieval-based Question Answering Systems (2021.findings-acl)
Copied to clipboard
| Challenge: | Question Answering systems are a core component of many commercial applications . answer sentence selection (AS2) models are trained to select the best answer sentence . |
| Approach: | They propose to train a sequence to sequence transformer model to generate an answer from a set of candidates. |
| Outcome: | The proposed model improves accuracy by 32 points over the state-of-the-art model on English AS2 datasets. |
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning (D19-58)
Copied to clipboard
| Challenge: | Existing methods focus on improving in-domain performance, leaving open the question of how they can generalize to out-of-domain and unseen RC tasks. |
| Approach: | They propose a multi-task learning framework that learns the shared representation across different tasks and builds on a large pre-trained language model and fine-tuned on multiple RC datasets. |
| Outcome: | The proposed framework improves the BERT-Large baseline by 8.39 and 7.22 respectively. |
Plug-and-Play Conversational Models (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Large conversational models that generate coherent and fluent responses often require large dialogue datasets. |
| Approach: | They propose and evaluate plug-and-play methods for controllable response generation . they demonstrate a high degree of control over the generated conversational responses . |
| Outcome: | The proposed method does not require further computation at decoding time and does not need fine-tuning of a large language model. |
Are Red Roses Red? Evaluating Consistency of Question-Answering Models (P19-1)
Copied to clipboard
| Challenge: | Existing question-answering systems are limited in their ability to test reasoning and comprehension. |
| Approach: | They propose a method to automatically extract implications from QA datasets to evaluate models' consistency . they use a heuristic to generate such questions and retrain models with implication-augmented data . |
| Outcome: | The proposed method shows that generated implications are well formed and valid . retraining with implication-augmented data improves consistency on both synthetic and human-generated implications. |
How to Represent Context Better? An Empirical Study on Context Modeling for Multi-turn Response Selection (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work on building a conversational system for open domain human-machine conversation is attracting more attention . early models concatenate all utterances or independently encode each dialogue turn, which may lead to an inadequate understanding of dialogue status. |
| Approach: | They propose to use a turn-aware context modeling layer to adapt existing models . they propose to model multi-turn contexts from the perspective of sequential relationship, local relationship, and query-alike manner . |
| Outcome: | The proposed method can be adapted to several advanced response selection models. |
CORE: Cooperative Training of Retriever-Reranker for Effective Dialogue Response Selection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to train retrieval-based dialogue systems are suboptimal . existing methods to optimize retrieval and rerank modules are sub-optimal, causing sub-optimum performance. |
| Approach: | They propose a retrieval-based dialogue system with a fast retriever and a smart response reranker that combine the best of both worlds. |
| Outcome: | The proposed method can learn from each other and evolve together . it can be used in industrial applications and has powered industrial applications. |