MC

Papers with MC

Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality (2023.eacl-main)

Copied to clipboard

Xinnian Liang, Shuangzhi Wu, Chenhao Cui, Jiaqi Bai, Chao Bian, Zhoujun Li

Dialogue Systems & Conversational Agents

Challenge:	Experimental results show that our model outperforms strong baselines on three public dialogue summarization datasets: CSDS, MC, and SAMSUM.
Approach:	They propose a topic-aware global-local centrality model to help select the salient context from all sub-topics.
Outcome:	The proposed model outperforms baselines on three public dialogue summarization datasets: CSDS, MC, and SAMSUM.

Evaluating Question Answering Evaluation (D19-58)

Copied to clipboard

Anthony Chen, Gabriel Stanovsky, Sameer Singh, Matt Gardner

Question Answering

Challenge:	Existing n-gram based QA metrics have a number of drawbacks and are not suitable for all extractive tasks.
Approach:	They propose to use BERTScore to evaluate translation for question answering (QA) they also explore whether existing n-gram based metrics are suitable for generative QA .
Outcome:	The proposed BERTScore metric fails to provide stronger correlation with human judgements .

Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learning (2022.naacl-industry)

Copied to clipboard

Angelo Ziletti, Alan Akbik, Christoph Berns, Thomas Herold, Marion Legler, Martina Viell

Language Models

Challenge:	Medical coding (MC) is an essential pre-requisite for reliable data retrieval and reporting.
Approach:	They propose a method to classify medical terms into standardized alphanumerical terms and codes . they use a combination of traditional BERT-based classification and a zero/few-shot learning approach .
Outcome:	The proposed approach outperforms baselines in the few-shot regime.

Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning (2022.findings-acl)

Copied to clipboard

Xiang Zhou, Yixin Nie, Mohit Bansal

Reasoning

Challenge:	Using distributed NLI, we show that models can capture human judgement distribution more effectively than the softmax baseline.
Approach:	They propose a new NLU task to predict the distribution of human judgements . they propose Monte Carlo, Deep Ensemble, Re-Calibration and Distribution Distillation methods to capture human judgement distributions.
Outcome:	The proposed methods perform better than the softmax baseline, but the results are still far below the estimated human upper-bound.

D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Models (2025.findings-acl)

Copied to clipboard

Grace Byun, Jinho D. Choi

Challenge:	Existing methods for generating generative models with open-ended generation rely on predefined distractors and are costly and time-consuming.
Approach:	They propose a ranking alignment and entropy analysis to evaluate distractors' quality.
Outcome:	The proposed model preserves ranking consistency and matches the entropy distribution of ground-truth distractors.

Revealing the Importance of Semantic Retrieval for Machine Reading at Scale (D19-1)

Copied to clipboard

Yixin Nie, Songhe Wang, Mohit Bansal

Information Retrieval

Challenge:	Recent advances in representation learning have separated progress in both IR and MC . few studies have examined the relationship between retrieval and comprehension at different levels of granularity for development of MRS systems.
Approach:	They propose a simple yet effective pipeline system with consideration on hierarchical semantic retrieval at both paragraph and sentence level and their potential effects on the downstream task.
Outcome:	The proposed system achieves state-of-the-art on the leaderboard test sets of both FEVER and HOTPOTQA.

Designing Templates for Eliciting Commonsense Knowledge from Pretrained Sequence-to-Sequence Models (2020.coling-main)

Copied to clipboard

Jheng-Hong Yang, Sheng-Chieh Lin, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, Jimmy Lin

Semantic Text Processing

Challenge:	Existing approaches to extract implicit knowledge from pretrained models are still unclear.
Approach:	They propose to use a template-based approach to extract implicit knowledge for commonsense reasoning on multiple-choice questions.
Outcome:	The proposed template can be extended to other MC tasks with contexts such as supporting facts in open-book question answering settings.

SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages (2025.coling-main)

Copied to clipboard

Gayane Ghazaryan, Erik Arakelyan, Isabelle Augenstein, Pasquale Minervini

Machine Translation

Challenge:	Question Answering datasets are scarce for languages other than English due to the cost and difficulties of collection and manual annotation.
Approach:	They propose a method for generating and validating QA datasets for low-resource languages . they use English data as context to generate synthetic multiple-choice (MC) question-answer pairs .
Outcome:	The proposed method maintains quality, reduces likelihood of factual errors, and circumvents costly annotation.

FinChart-Bench: Benchmarking Financial Chart Comprehension in Vision-Language Models (2026.acl-long)

Copied to clipboard

Dong Shu, Haoyang Yuan, Yuchen Wang, Yanguang Liu, Huopu Zhang, Mengnan Du

Multimodality

Challenge:	FinChart-Bench is the first benchmark specifically focused on real-world financial charts.
Approach:	They propose a benchmark specifically focused on real-world financial charts.
Outcome:	The proposed benchmark evaluates 26 state-of-the-art LVLMs on FinChart-Bench.

SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages (L18-1)

Copied to clipboard

Siamak Barzegar, Brian Davis, Manel Zarrouk, Siegfried Handschuh, Andre Freitas

Semantic Text Processing

Challenge:	SemR-11 is a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages.
Approach:	This paper describes a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages.
Outcome:	The dataset is a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages.

Parsing for Mauritian Creole Using Universal Dependencies (2024.lrec-main)

Copied to clipboard

Neha Ramsurrun, Rolando Coto-Solano, Michael Gonzalez

Syntactic Text Processing

Challenge:	a paper demonstrates the construction of a 161-sentence treebank for Mauritian Creole . the parser trained with UD reached F1 scores of UPOS=86.2, UAS=80.8 and LAS=69.8.
Approach:	They propose to use Universal Dependencies to train a parser for Mauritian Creole . they demonstrate the construction of a 161-sentence treebank and evaluate the performance .
Outcome:	The proposed treebank achieves F1 scores compared to models for other under-resourced Creole languages.

DDO: Dual-Decision Optimization for LLM-Based Medical Consultation via Multi-Agent Collaboration (2025.emnlp-main)

Copied to clipboard

Zhihao Jia, Mingyi Jia, Junwen Duan, Jianxin Wang

Challenge:	Existing LLMs fail to capture the dual nature of medical consultation (MC) this mismatch often results in ineffective symptom inquiry and unreliable disease diagnosis.
Approach:	They propose a novel LLM-based framework that performs Dual-Decision Optimization by decoupling the two sub-tasks and optimizing them with distinct objectives through a collaborative multi-agent workflow.
Outcome:	The proposed framework outperforms existing LLM-based approaches on three real-world MC datasets and achieves competitive performance with state-of-the-art generation-based methods.