An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model (2021.emnlp-main)
Copied to clipboard
| Challenge: | Multi-turn response selection models have shown comparable performance to humans in several benchmark datasets, but in the real environment, they often have weaknesses, such as giving the highest score to the wrong response candidate containing several keywords related to the context. |
| Approach: | They propose to build a robust multi-turn response selection model in an adversarial environment and to use it to evaluate weaknesses. |
| Outcome: | The proposed model makes incorrect predictions based heavily on superficial patterns without a comprehensive understanding of the context. |
Similar Papers
MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts? (2025.findings-emnlp)
Copied to clipboard
Muntasir Wahed, Xiaona Zhou, Kiet A. Nguyen, Tianjiao Yu, Nirav Diwan, Gang Wang, Dilek Hakkani-Tür, Ismini Lourentzou
| Challenge: | Recent advances in Large Language Models have significantly enhanced their code generation capabilities, but their robustness against adversarial misuse remains underexplored. |
| Approach: | They introduce a code decomposition attack where a malicious coding task is broken down into subtasks across multiple conversational turns to evade safety filters. |
| Outcome: | The proposed code decomposition attacks exploits multi-turn malicious coding prompts . the proposed model improves rejection rates while preserving coding ability . |
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. |
A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Using large language models, we evaluated their robustness on multiple datasets. |
| Approach: | They propose a new metric for assessing model robustness by empirical evaluation of several models on multiple datasets. |
| Outcome: | The proposed metric is based on a set of datasets that are constructed by introducing naturally-occurring, non-malicious perturbations or by generating semantically equivalent paraphrases of input questions or statements. |
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots (D19-1)
Copied to clipboard
| Challenge: | Existing studies focus on matching candidate responses with every context utterance, but it also brings noise signals and unnecessary information. |
| Approach: | They propose a multi-hop selector network to match context with candidate responses . they propose to use a selector to filter the relevant utterances as context . |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public multi-turn dialogue datasets. |
Robustness and Adversarial Examples in Natural Language Processing (2021.emnlp-tutorials)
Copied to clipboard
| Challenge: | This tutorial aims to raise awareness of practical concerns about NLP robustness . it aims at addressing the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
| Approach: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it reviews recent studies on analyzing the weakness of NLP systems when facing adversarial inputs . |
| Outcome: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it will examine the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
Intra-/Inter-Interaction Network with Latent Interaction Modeling for Multi-turn Response Selection (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for multi-turn response selection are not practical as the turns of conversations vary. |
| Approach: | They propose to use latent interaction modeling to model multi-level interactions between utterance and response. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three multi-turn response selection benchmark datasets. |
Data Selection for Multi-turn Dialogue Instruction Tuning (2026.findings-acl)
Copied to clipboard
| Challenge: | Instruction-tuned language models often use noisy multi-turn dialogue datasets with topic drift, repetitive chitchat, and mismatched answer formats across turns. |
| Approach: | They propose a dialogue-level framework that scores whole conversations rather than isolated turns. |
| Outcome: | The proposed framework outperforms strong single-turn selectors, dialogue-level LLM scorers and heuristic baselines on three multi-turn benchmarks and an in-domain Banking test set. |
The Fragility of Multi-Treebank Parsing Evaluation (2022.coling-1)
Copied to clipboard
| Challenge: | a limitation in NLP evaluation lies in the association between solving a dataset versus solving . authors often run their models only in a handful of treebanks . |
| Approach: | They propose to run a large-scale experiment on a single treebank and compare them on many parsers whose scores are available. |
| Outcome: | The proposed model can be biased on a single treebank and spurious effects can be avoided. |
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLP (2023.eacl-main)
Copied to clipboard
| Challenge: | Modern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct. |
| Approach: | They propose to clarify the current situation and plot a course for meaningful progress in fair learning by making clear inter-relations among the current gamut of methods and their relation to fairness theory. |
| Outcome: | The proposed approach addresses the practical problem of model selection, which involves a trade-off between fairness and accuracy and has led to systemic issues in fairness research. |
Revisiting Multi-Domain Machine Translation (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing approaches to handle multi-domain machine translation systems are lacking due to the variability of data. |
| Approach: | They propose to use domain adaptation methods to handle situations where a sample of matched sentences is available in training and where only samples of source-side sentences are available. |
| Outcome: | The proposed model is able to handle multiple domains and their expectations with respect to performance. |