AUXILIARY TASKS

Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia (2022.naacl-main)

Challenge:	Existing methods to evaluate factual consistency in text summarization neglect the intrinsic cause of factual inconsistency or rely on auxiliary tasks.
Approach:	They propose a method to evaluate the factual consistency in text summarization via counterfactual estimation, which formulates the causal relationship between source document, generated summary, and the language prior.
Outcome:	The proposed metric improves correlation with human judgments and convenience of usage on three public abstractive text summarization datasets.

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Samee Ibraheem, Gaoyue Zhou, John DeNero

Text Classification

Challenge:	Using neural networks, we can model the impact of speaker role on language use through the game of Mafia.
Approach:	They analyze the effect of speaker role on language use through the game of Mafia, in which players are assigned either an honest or a deceptive role.
Outcome:	The proposed model outperforms a standard BERT-based text classification approach on two auxiliary tasks and identifies features that distinguish between player roles.

Few-Shot and Zero-Shot Learning for Historical Text Normalization (D19-61)

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Marcel Bollmann, Natalia Korchagina, Anders Søgaard

An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition (2022.acl-long)

Challenge:	Historical text normalization often relies on small training datasets.
Approach:	They evaluate 63 multi-task learning configurations for sequence-to-sequence-based historical text normalization across ten datasets from eight languages.
Outcome:	The proposed learning architecture outperforms the simple, but strong identity baseline.

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Zhuoran Li, Chunming Hu, Xiaohui Guo, Junfan Chen, Wenyi Qin, Richong Zhang

A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages (2021.eacl-srw)

Challenge:	Existing models for named entity recognition only consider the potential transferability between two identical tasks across both domains.
Approach:	They propose to use a similarity metric model to improve cross-lingual named entity recognition task on target domain.
Outcome:	Empirical studies on 7 different languages confirm the effectiveness of the proposed model.

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Jivnesh Sandhan, Amrith Krishna, Ashim Gupta, Laxmidhar Behera, Pawan Goyal

Language Scaling for Universal Suggested Replies Model (2021.naacl-industry)

Challenge:	Neural dependency parsing has been a success for many domains and languages, but the bottleneck of massive labelled data limits its effectiveness for low resource languages.
Approach:	They propose to use morphological knowledge to improve dependency parsing for morphology rich languages in a low-resource setting to perform experiments.
Outcome:	The proposed method achieves an average gain of 2 points (UAS) and 3.6 points (LAS) on 10 MRLs in low-resource settings.

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Qianlan Ying, Payal Bajaj, Budhaditya Deb, Yu Yang, Wei Wang, Bojia Lin, Milad Shokouhi, Xia Song, Yang Yang, Daxin Jiang

Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation (D19-56)

Challenge:	We consider scaling automated suggested replies (SR) to multiple languages for a commercial email application.
Approach:	They propose a multi-lingual multi-task continual learning framework with auxiliary tasks and language adapters to train universal language representation across regions.
Outcome:	The proposed model reduces catastrophic forgetting and improves cross-lingual transfer across languages while reducing training costs.

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Poorya Zaremoodi, Gholamreza Haffari

A Unified Sequence Labeling Model for Emotion Cause Pair Extraction (2020.coling-main)

Challenge:	Neural Machine Translation suffers from the lack of bilingual data in low-resource scenarios.
Approach:	They propose to inject inductive biases into Neural Machine Translation (NMT) using auxiliary syntactic and semantic tasks.
Outcome:	The proposed approach improves translation quality by reweighing training data of main and auxiliary tasks based on their contributions to generalisability of main task.

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Xinhong Chen, Qing Li, Jianping Wang

DiscoverGPT: Multi-task Fine-tuning Large Language Model for Related Table Discovery (2025.findings-naacl)

Challenge:	Existing methods for emotion-cause pair extraction cannot distinguish emotion-caused pairs from each other . Existing approaches may suffer from possible cascading errors .
Approach:	They propose to assign emotion type labels to emotion and cause clauses so that they can be easily distinguished.
Outcome:	The proposed method can extract multiple emotion-cause pairs in an end-to-end fashion.

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Xuming Hu, Xiao Qin, Chuan Lei, Asterios Katsifodimos, Zhengyuan Shen, Balasubramaniam Srinivasan, Huzefa Rangwala

Contextualization Distillation from Large Language Model for Knowledge Graph Completion (2024.findings-eacl)

Challenge:	Existing methods to learn and evaluate the table semantic relatedness of tabular data are based on pretrain-and-finetune paradigms.
Approach:	They propose a multi-task fine-tuning framework that holistically discovers and leverages the intricate relationships among the supervisions to optimize the performance on the data discovery task.
Outcome:	The proposed framework outperforms the best performing baseline by up to 7% in F1 score.

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Dawei Li, Zhen Tan, Tianlong Chen, Huan Liu

Dual-Encoder Transformers with Cross-modal Alignment for Multimodal Aspect-based Sentiment Analysis (2022.aacl-main)

Challenge:	Existing knowledge graph completion models lack textual information, which limits their performance . a plug-in-and-play approach is needed to train small models in descriptive context .
Approach:	They propose a plug-in-and-play approach to knowledge graph completion that prompts LLMs to generate descriptive context.
Outcome:	The proposed method improves performance on Wikipedia articles and synset definitions.

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Zhewen Yu, Jin Wang, Liang-Chih Yu, Xuejie Zhang

Sentiment Analysis

Challenge:	Multimodal aspect-based sentiment analysis (MABSA) aims to extract aspect terms from text and image pairs, and then analyze their corresponding sentiment.
Approach:	They propose a dual-encoder transformer with cross-modal alignment to extract aspect terms from text and image pairs and then analyze their corresponding sentiments.
Outcome:	The proposed approach outperforms existing methods on two benchmarks.

Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity (2021.emnlp-main)

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Po-Nien Kung, Sheng-Siang Yin, Yi-Cheng Chen, Tse-Hsuan Yang, Yun-Nung Chen

Multitask Parsing Across Semantic Representations (P18-1)

Challenge:	Multi-task auxiliary learning uses a set of relevant auxiliary tasks to improve performance of a primary task.
Approach:	They propose a time-efficient sampling method to select the most beneficial sub-datasets from the auxiliary tasks to achieve efficient multi-task auxiliary learning.
Outcome:	The proposed method significantly outperforms random sampling and ST-DNN on three benchmark datasets.

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Daniel Hershcovich, Omri Abend, Ari Rappoport

Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees (N18-2)

Challenge:	UCCA parsing is a test case for multitask learning, with auxiliary tasks AMR, SDP and Universal Dependencies (UD) . Semantic parsers have arguably yet to reach their full potential due to the limited amount of semantically annotated training data.
Approach:	They propose a general transition-based parser that can parse UCCA, AMR, SDP and Universal Dependencies (UD) they use a transition-driven learning architecture and a uniform transition-basic learning architecture to train the parsers.
Outcome:	The proposed parser improves UCCA, AMR, SDP and Universal Dependencies (UD) parsing over training in English, German and French.

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Ola Rønning, Daniel Hardt, Anders Søgaard

Generating Equation by Utilizing Operators : GEO model (2020.coling-main)

Challenge:	sluice resolution in english is the problem of finding antecedents of wh-fronted ellipses . previous work relied on hand-crafted features over syntax trees that scale poorly to other languages and domains .
Approach:	They propose a model that uses partial parsing to find antecedents of wh-fronted ellipses in english . their model significantly outperforms previous work on available newswires .
Outcome:	The proposed model outperforms the only previous work on available newswires.

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Kyung Seo Ki, Donggeon Lee, Bugeun Kim, Gahgene Gweon

Self-supervised Regularization for Text Classification (2021.tacl-1)

Challenge:	Existing neural models that use hand-crafted features are expensive and lack domain-specific knowledge.
Approach:	They propose a GEO model that uses operator-based features to generate equations using natural language sentences.
Outcome:	The proposed model outperforms state-of-the-art models on two datasets and 82.1% in ALG514.

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Meng Zhou, Zechen Li, Pengtao Xie

Dynamic Multi-Level Multi-Task Learning for Sentence Simplification (C18-1)

Challenge:	Text classification models are prone to overfitting when limited texts are available for training.
Approach:	They propose a data-dependent regularization approach based on self-supervised learning . they define auxiliary tasks on input data without using human-provided labels .
Outcome:	Experiments on 17 text classification datasets demonstrate the effectiveness of the proposed method.

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Han Guo, Ramakanth Pasunuru, Mohit Bansal

Paraphrasing

Challenge:	Sentence simplification is the task of improving readability and understandability of an input text.
Approach:	They propose a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model and a novel ‘multi-level’ soft sharing approach where each auxiliary task shares different (higher versus lower) level layers of the model.
Outcome:	The proposed model outperforms competing simplification systems in SARI and FKGL automatic metrics, and human evaluation.

An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models (2020.tacl-1)

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Lifu Tu, Garima Lalwani, Spandana Gella, He He

GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning (2021.findings-acl)

Challenge:	Recent work shows that pre-trained language models perform poorly on challenging datasets where spurious correlations do not hold.
Approach:	They propose to use multi-task learning to improve generalization from minority examples . they propose to combine MTL with auxiliary tasks to improve performance .
Outcome:	The proposed model generalizes from minority examples without hurting in-distribution performance.

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Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric Xing, Liang Lin

Temporal Cognitive Tree: A Hierarchical Modeling Approach for Event Temporal Relation Extraction (2024.findings-emnlp)

Challenge:	Existing methods to solve geometric problems are dependent on handcraft rules and limited on small-scale datasets.
Approach:	They propose a Geometric Question Answering dataset with 5,010 geometric problems with corresponding annotated programs to illustrate the solving process.
Outcome:	The proposed method is significantly lower than human performance on the proposed dataset than on a publicly available dataset.

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Wanting Ning, Lishuang Li, Xueyang Qin, Yubo Feng, Jingyao Tang

Agenda-Driven Question Generation: A Case Study in the Courtroom Domain (2024.lrec-main)

Challenge:	Recent studies focus on locating relative position of event pairs on timeline . hierarchical modeling approach neglects multidimensional information in temporal relation and hierarchy of reasoning.
Approach:	They propose a novel hierarchical modeling approach that mimics human logical reasoning by introducing a Temporal Cognitive Tree.
Outcome:	The proposed model outperforms existing methods on TB-Dense and MATRES datasets.

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Yi Fung, Anoop Kumar, Aram Galstyan, Heng Ji, Prem Natarajan

An Auxiliary Task Boosted Multi-task Learning Method for Service Account Retrieval with Limited Human Annotation (2023.emnlp-industry)

Challenge:	Existing automated question generation methods focus on unstructured text and lack agenda and background documents as context.
Approach:	They propose to leverage large language models for CourtQG by fine-tuning them on two auxiliary tasks, agenda explanation and question type prediction.
Outcome:	The proposed method generates better questions according to standard metrics when compared to several baselines.

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Yuanzhou Yao, Zhao Zhang, Kaijia Yang, Huasheng Liang, Qiang Yan, Yongjun Xu

Unified Contextual Query Rewriting (2023.acl-industry)

Challenge:	Existing approaches to service account retrieval have limited human annotation, resulting in labor-intensive and time-consuming tasks.
Approach:	They propose an Auxiliary task Boosted Multi-Task Learning method which introduces multiple auxiliary tasks and enhances the performance of the main task, service account retrieval.
Outcome:	The proposed method improves the performance of the main task, service account retrieval.

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Yingxue Zhou, Jie Hao, Mukund Rungta, Yang Liu, Eunah Cho, Xing Fan, Yanbin Lu, Vishal Vasudevan, Kellen Gillespie, Zeynab Raeesy

Paraphrasing

Challenge:	Large-scale conversational AI agents such as Alexa, Siri, and Google Assistant are becoming increasingly popular in real-world applications to assist users in daily life.
Approach:	They propose a unified contextual query rewriting model that unifies QR for friction reduction and contextual carryover . they leverage the text-to-text unified framework which uses independent tasks with weighted loss to account for task importance .
Outcome:	The proposed model reduces friction and contextual carryover by using multiple auxiliary tasks.

Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (P18-1)

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Han Guo, Ramakanth Pasunuru, Mohit Bansal

Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning (D18-1)

Challenge:	Recent advances on abstractive summarization have allowed substantial improvements in the quality of the model, but there is still scope for improvement.
Approach:	They propose novel multi-task architectures with high-level layer-specific sharing across multiple encoder and decoder layers of the three tasks and soft-sharing mechanisms.
Outcome:	The proposed model improves on the CNN/DailyMail and Gigaword datasets and on the DUC-2002 transfer setup.

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Sotiris Lamprinidis, Daniel Hardt, Dirk Hovy

Data-Centric Improvements for Enhancing Multi-Modal Understanding in Spoken Conversation Modeling (2025.findings-acl)

Challenge:	Pre-trained word embeddings provide significant improvements over untrained embeddables . Feature analysis reveals structural patterns of headline popularity .
Approach:	They use a multi-task GRU network to model headline popularity . they find that pre-trained word embeddings provide significant improvements over untrained embeddables .
Outcome:	The proposed model improves on pre-trained word embeddings and untrained embeddables . it also improves with the combination of two auxiliary tasks, news-section prediction and part-of-speech tagging .

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Maximillian Chen, Ruoxi Sun, Sercan O Arik

Natural Language Interfaces

Challenge:	Conversational assistants are increasingly popular across diverse real-world applications . speech data constitute high-dimensional signals that are difficult to model even for frontier models .
Approach:	They propose a data-centric customization approach for enhancing multimodal understanding in conversational speech modeling.
Outcome:	The proposed model achieves state-of-the-art on the Spoken-SQuAD benchmark using 10% of training data with open-weight models.

“Why do I feel offended?” - Korean Dataset for Offensive Language Identification (2023.findings-eacl)

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San-Hee Park, Kang-Min Kim, O-Joun Lee, Youjin Kang, Jaewon Lee, Su-Min Lee, SangKeun Lee

Text Classification

Challenge:	Existing methods for detecting offensive content rely on labeled datasets, but few consider low-resource languages with relatively less data available for training.
Approach:	They propose to use Korean as a dataset for offensive language identification . they propose to perform abusive language detection and sentiment analysis to help identify offensive languages.
Outcome:	The proposed datasets improve the performance of offensive language identification in Korean, while the existing methods are limited.

Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension (2020.acl-main)

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Fei Yuan, Linjun Shou, Xuanyu Bai, Ming Gong, Yaobo Liang, Nan Duan, Yan Fu, Daxin Jiang

Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks (2022.findings-naacl)

Challenge:	Existing approaches to improve machine reading comprehension performance on low resource languages are limited due to the lack of sufficient training data.
Approach:	They propose to use a mixed MRC task to translate the question to other languages and build cross-lingual question-passage pairs.
Outcome:	The proposed task improves on two cross-lingual MRC datasets.

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Şaziye Betül Özateş, Arzucan Özgür, Tunga Gungor, Özlem Çetinoğlu

Multimodal Topic-Enriched Auxiliary Learning for Depression Detection (2020.coling-main)

Challenge:	Code-switching dependency parsing is a challenging task due to the scarcity of necessary resources and structural difficulties embedded in code-switch languages.
Approach:	They propose to use sequence labeling models as auxiliary tasks for code-switched dependency parsing in a semi-supervised scheme and acquire state-of-the-art scores on all studied languages.
Outcome:	The proposed model outperforms the previous model by 7.4 LAS points on average on all of the studied languages.

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Minghui An, Jingjing Wang, Shoushan Li, Guodong Zhou

Bias Mitigation in Machine Translation Quality Estimation (2022.acl-long)

Challenge:	Existing studies on depression detection rely on textual and visual content to determine whether a human being is depressed or non-depressed.
Approach:	They propose a multimodal topic-enriched Auxiliary Learning approach that captures topic information from texts and images for depression detection.
Outcome:	The proposed approach improves the performance of the primary task by using topic information from text and images.

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Hanna Behnke, Marina Fomicheva, Lucia Specia

Machine Translation

Challenge:	despite advances in machine translation, the accuracy and fluency of translations cannot be guaranteed without a reference translation.
Approach:	They propose to use auxiliary tasks to mitigate partial input bias . they aim to train a multitask architecture with an auxiliary binary classification task .
Outcome:	The proposed models reduce partial input bias while maintaining the overall performance.

Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization (2021.findings-emnlp)

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Junpeng Liu, Yanyan Zou, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Caixia Yuan, Xiaojie Wang

Neural Machine Translation for Bilingually Scarce Scenarios: a Deep Multi-Task Learning Approach (N18-1)

Challenge:	Existing methods to abstractly summarize dialogues are limited to two or more interlocutors.
Approach:	They propose to use existing document summarization models to capture the various topic information of a conversation and outline salient facts for the captured topics.
Outcome:	The proposed method significantly outperforms baselines and achieves new state-of-the-art performance on benchmark datasets.

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Poorya Zaremoodi, Gholamreza Haffari

Target-oriented Fine-tuning for Zero-Resource Named Entity Recognition (2021.findings-acl)

Challenge:	Neural machine translation requires large amount of parallel training text to learn a reasonable quality translation model.
Approach:	They propose a multi-task learning approach that leverages monolingual linguistic resources in the source side of a machine translation task.
Outcome:	The proposed approach is effective on three translation tasks: English-to-French, English- to-Farsi, and English-à-Vietnamese.

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Ying Zhang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising (2022.findings-naacl)

Challenge:	Named entity recognition (NER) is one of the fundamental tasks in natural language processing.
Approach:	They propose four practical guidelines to guide knowledge transfer and task finetuning . they propose a framework to exploit data from three aspects in a unified training manner .
Outcome:	The proposed framework improves on six benchmarks and shows that it is state-of-the-art in five languages.

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Kuan Xu, Yongbo Wang, Yongliang Wang, Zihao Wang, Zujie Wen, Yang Dong

Programming Languages in NLP

Challenge:	Using sketch-based slot filling, text-to-SQL models suffer from over-complexity . et al., e.al., and d.albert, dr., propose a novel method for text- to-Sql generation .
Approach:	They propose to train sequence-to-sequence model with Schema-aware Denoising . they propose a clause-sensitive execution guided (EG) decoding strategy .
Outcome:	The proposed method improves performance in schema linking and grammar correctness . it also establishes new state-of-the-art on the WikiSQL benchmark .

Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases (2022.coling-1)

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Xixin Hu, Xuan Wu, Yiheng Shu, Yuzhong Qu

Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem (2022.findings-acl)

Challenge:	Existing generation-based KBQA methods that translate natural language questions to executable logical forms are proving promising but noise introduced can lead to incorrect results.
Approach:	They propose a Generation-based KBQA method that uses auxiliary information to enhance logical form generation by combining unseen KB items with novel combinations.
Outcome:	The proposed method achieves state-of-the-art results on ComplexWebQuestions and WebQuestIONSSP datasets.

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Khalil Mrini, Shaoliang Nie, Jiatao Gu, Sinong Wang, Maziar Sanjabi, Hamed Firooz

Towards Making a Dependency Parser See (D19-1)

Challenge:	Existing methods for entity linking do not use a knowledge base or candidate sets.
Approach:	They propose an autoregressive entity linking model that is trained with two auxiliary tasks and learns to re-rank generated samples at inference time.
Outcome:	The proposed model improves on two biomedical datasets and a news domain dataset without the use of a knowledge base or candidate sets.

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Michalina Strzyz, David Vilares, Carlos Gómez-Rodríguez

Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions (2021.eacl-main)

Challenge:	Eye trackers and gaze features collected from them have been recently applied to natural language processing (NLP) tasks such as part-of-speech tagging.
Approach:	They propose to leverage eye-tracking data in an RNN dependency parser when no aggregated or token-level gaze features are used at inference time.
Outcome:	The proposed model can be used to improve performance on non-gazed treebanks.

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Pere-Lluís Huguet Cabot, David Abadi, Agneta Fischer, Ekaterina Shutova

Challenge:	Populist rhetoric has risen across the political sphere in recent years, but computational approaches to it have been scarce.
Approach:	They propose a dataset of 6861 reddit comments annotated for populist attitudes and a set of multi-task learning models that leverage emotion and group identification as auxiliary tasks.
Outcome:	The proposed models leverage emotion and group identification as auxiliary tasks to model populist rhetoric tasks.

Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization (2023.acl-long)

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Yunlong Liang, Fandong Meng, Jinan Xu, Jiaan Wang, Yufeng Chen, Jie Zhou

TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching (2020.coling-main)

Challenge:	Existing studies on multimodal abstractive summarization focus on how to use extracted visual features to produce a concise summary given the multimodal data.
Approach:	They propose to improve the visual quality of the multimodal abstractive summarization model by capturing summary-oriented visual features.
Outcome:	The proposed approach achieves state-of-the-art under 44 languages and is highly effective on high-resource English datasets.

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Heng Gong, Yawei Sun, Xiaocheng Feng, Bing Qin, Wei Bi, Xiaojiang Liu, Ting Liu

Re-entry Prediction for Online Conversations via Self-Supervised Learning (2021.findings-emnlp)

Challenge:	Recent studies show that pre-trained language models can produce informative and fluent text with the help of large-scale datasets, but they suffer insufficient learning problem with limited training data.
Approach:	They propose to use table transformation module with template to rewrite structured table in natural language as input for GPT-2 and exploit multi-task learning with two auxiliary tasks to preserve table’s structural information.
Outcome:	The proposed model outperforms existing systems on most few-shot settings.

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Lingzhi Wang, Xingshan Zeng, Huang Hu, Kam-Fai Wong, Daxin Jiang

Dialogue Systems & Conversational Agents

Challenge:	Existing work on re-entry prediction ignores conversation thread patterns and repeated engagement of target users.
Approach:	They propose to use conversation thread patterns to predict whether a user will come back to a conversation they once participated in to train a model on labels that are automatically derived from the data.
Outcome:	The proposed task outperforms the state-of-the-art models on two social media datasets with fewer parameters and faster convergence.

UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model (2023.findings-emnlp)

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Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, Qin Jin, Liang He, Xin Lin, Fei Huang

UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking (2024.findings-naacl)

Challenge:	Existing studies for visually-situated language understanding have shown shallow zero-shot visual text recognition ability when fed a low-resolution image with salient text information.
Approach:	They propose a model for universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM) their model is jointly finetuned on a wide range of visually situated language understanding tasks via a unified instruction format.
Outcome:	The proposed model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks across 5 domains: documents, tables, charts, natural images, and webpage screenshots.

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Chuang Li, Yan Zhang, Min-Yen Kan, Haizhou Li

Auxiliary tasks to boost Biaffine Semantic Dependency Parsing (2022.findings-acl)

Challenge:	Existing methods for zero-shot dialogue state tracking (DST) ignore unlabelled data in the target domain.
Approach:	They propose to transform zero-shot dialogue state tracking into few-shot DST by utilising unlabelled data via joint and self-training methods.
Outcome:	The proposed method improves joint goal accuracy by 8% on general language models in zero-shot scenarios, and can be used in many domains.

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Marie Candito

ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs (2022.findings-naacl)

Challenge:	Semantic dependency parsing (SDP) is a task of producing a dependency graph for a sentence.
Approach:	They propose to use simple auxiliary tasks that introduce some form of interdependence between arcs to circumvent such an independence of decision.
Outcome:	The proposed method shows modest but systematic performance gains on a near-state-of-the-art baseline using transformer-based contextualized representations.

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Liang Chen, Peiyi Wang, Runxin Xu, Tianyu Liu, Zhifang Sui, Baobao Chang

Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues (2023.findings-eacl)

Challenge:	Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations.
Approach:	They propose to use auxiliary tasks which are semantically or formally related to enhance AMR parsing.
Outcome:	The proposed method achieves state-of-the-art performance on benchmarks especially in topology-related scores.

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Chuyuan Li, Patrick Huber, Wen Xiao, Maxime Amblard, Chloe Braud, Giuseppe Carenini

Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval (2022.findings-naacl)

Challenge:	Discourse processing suffers from data sparsity, especially for dialogues . a variety of discourse frameworks have been proposed to extract discourse information from dialogues.
Approach:	They propose unsupervised and semi-supervised methods to infer latent discourse structures for dialogues based on attention matrices from Pre-trained Language Models.
Outcome:	The proposed methods achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for the unsupervised and semi-supervised methods, respectively.

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Zhihao Fan, Zhongyu Wei, Zejun Li, Siyuan Wang, Xuanjing Huang, Jianqing Fan

MTRec: Multi-Task Learning over BERT for News Recommendation (2022.findings-acl)

Challenge:	Existing approaches to retrieve hard negative sentences are limited in the scale of the dataset thus fail to identify negative samples of high difficulty for every image.
Approach:	They propose to use a model to generate synthetic negative sentences with higher difficulty by masking and refilling the images and performing word discrimination and word correction tasks to improve retrieval and generation.
Outcome:	The proposed model generates synthetic negative sentences with higher difficulty on MS-COCO and Flickr30K and is robust and faithful to state-of-the-art training.

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Qiwei Bi, Jian Li, Lifeng Shang, Xin Jiang, Qun Liu, Hanfang Yang

Self-Supervised Learning for Contextualized Extractive Summarization (P19-1)

Challenge:	Existing news recommendation methods learn news representations solely based on news titles. Existing methods only utilize title information and neglect other valuable news information such as categories and entities.
Approach:	They propose a multi-task method to incorporate multi-field information into BERT, which improves its news encoding capability.
Outcome:	Extensive experiments on the MIND news recommendation benchmark show the proposed method is effective.

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Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang

Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media (N19-1)

Challenge:	Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss . previous work builds an end-to-end system to learn to choose sentences without explicitly modeling document context .
Approach:	They propose three auxiliary pre-training tasks that learn to capture the document context in a self-supervised fashion.
Outcome:	The proposed models outperform existing models on a CNN/DM dataset.

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Ramy Baly, Georgi Karadzhov, Abdelrhman Saleh, James Glass, Preslav Nakov

Continual Learning for Text Classification with Information Disentanglement Based Regularization (2021.naacl-main)

Challenge:	a number of fact-checking initiatives have been launched, both manual and automatic, but the whole enterprise remains in a state of crisis.
Approach:	They propose a multi-task ordinal regression framework that models trustworthiness estimation and political ideology detection of entire news outlets.
Outcome:	The proposed model outperforms models that target the problems in isolation.

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Yufan Huang, Yanzhe Zhang, Jiaao Chen, Xuezhi Wang, Diyi Yang

Symbolization, Prompt, and Classification: A Framework for Implicit Speaker Identification in Novels (2023.findings-emnlp)

Challenge:	Existing continual learning methods focus on preserving knowledge from previous tasks . Continual learning is a useful tool for learning over time, but it is not always possible to generalize to new tasks.
Approach:	They propose a disentanglement-based regularization method for continual learning on text classification that disentangles text hidden spaces into generic representations and regularizes them differently to constrain knowledge required to generalize.
Outcome:	The proposed method disentangles text hidden spaces into representations that are generic to all tasks and representations specific to each individual task.

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Yue Chen, Tianwei He, Hongbin Zhou, Jia-Chen Gu, Heng Lu, Zhen-Hua Ling

Selecting Stickers in Open-Domain Dialogue through Multitask Learning (2022.findings-acl)

Challenge:	Existing methods for speaker identification in novel dialogues are limited to handling explicit narrative patterns and complex cases.
Approach:	They propose a framework which identifies implicit speakers in novels via symbolization, prompt, and classification.
Outcome:	The proposed framework outperforms existing methods by 4.8% accuracy on the web novel collection, which reduces 47% of speaker identification errors, and outperfies the emerging ChatGPT.

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Zhexin Zhang, Yeshuang Zhu, Zhengcong Fei, Jinchao Zhang, Jie Zhou

Dialogue Systems & Conversational Agents

Challenge:	Existing methods to select appropriate stickers in open-domain dialogues have not been explored.
Approach:	They propose a multitask learning method consisting of three auxiliary tasks to combine multimodal information to enhance the understanding of dialogue history, emotion and semantic meaning of stickers.
Outcome:	The proposed model can combine multimodal information and achieve significantly higher accuracy over strong baselines.

Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling (2021.naacl-main)

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Aaron Mueller, Mark Dredze

Span Selection Pre-training for Question Answering (2020.acl-main)

Challenge:	Topic models can augment or replace bag-of-words inputs with pre-trained transformer-based word prediction models.
Approach:	They propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual topic modeling.
Outcome:	The proposed methods improve both monolingual and zero-shot polylingual topic modeling.

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Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, Lin Pan, G P Shrivatsa Bhargav, Dinesh Garg, Avi Sil

Natural Language Interfaces

Challenge:	Pre-trained BERTs provide large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA).
Approach:	They propose a new pre-training task inspired by reading comprehension to better align the pre- training from memorization to understanding.
Outcome:	The proposed model outperforms BERT-BASE and BERT LARGE on a new dataset and improves answer prediction F1 by 4 points and supporting fact prediction F1.

SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling (N19-1)

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Peng Lu, Ting Bai, Philippe Langlais

2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition (2023.findings-emnlp)

Challenge:	Multi-task learning (MTL) has been studied for sequence labeling tasks . auxiliary tasks are selected specifically to improve performance of a target task .
Approach:	They propose a shared-cell long-short-term memory cell which contains shared parameters that can learn from all tasks and task-specific parameters that could learn task-related information.
Outcome:	The proposed model can learn from all tasks and task-specific parameters.

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Jiasheng Zhang, Xikai Liu, Xinyi Lai, Yan Gao, Shusen Wang, Yao Hu, Yiqing Lin

MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types (2023.findings-acl)

Challenge:	Named Entity Recognition (NER) tasks are a fundamental task of natural language processing (NLP).
Approach:	They propose a text-to-text framework for Few-Shot Named Entity Recognition (NER) that employs instruction finetuning and auxiliary tasks to enhance the model's understanding of entity types in the overall semantic context of a sentence.
Outcome:	The proposed framework outperforms existing Few-Shot NER methods and remains competitive with state-of-the-art NER algorithms.

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Keerthiram Murugesan, Sarathkrishna Swaminathan, Soham Dan, Subhajit Chaudhury, Chulaka Gunasekara, Maxwell Crouse, Diwakar Mahajan, Ibrahim Abdelaziz, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Alexander Gray

Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks (2020.emnlp-main)

Challenge:	Existing evaluation metrics for machine text are inadequate to capture quality of text . a recent study has focused on task-specific evaluation metrics or on properties of machine-generated text based on mismatch errors .
Approach:	They propose a new evaluation scheme based on fine-grained mismatch errors . they propose 13 mismatch error types to guide the model for better prediction of human judgments .
Outcome:	The proposed evaluation scheme is based on mismatch errors in 7 NLP tasks . the mismatch error types guide the model for better prediction of human judgments .

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Yufan Zhao, Can Xu, Wei Wu

All-in-one: Multi-task Learning for Rumour Verification (C18-1)

Challenge:	Existing approaches to multi-turn response generation for open-domain dialogues have a complexity problem . auxiliary tasks that relate to context understanding can guide the learning of the generation model .
Approach:	They propose a multi-turn response generation model that has a simple structure yet can effectively leverage conversation contexts for response generation.
Outcome:	The proposed model outperforms state-of-the-art models in response quality and human judgment . it also enjoys a faster decoding process .

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Elena Kochkina, Maria Liakata, Arkaitz Zubiaga

Toxicity, Morality, and Speech Act Guided Stance Detection (2023.findings-emnlp)

Challenge:	Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline . previous work focused on rumor detection, rumou tracking and stance classification as separate components .
Approach:	They propose a multi-task learning approach that allows joint training of main and auxiliary tasks, improving the performance of rumour verification.
Outcome:	The proposed approach improves the performance of rumour verification by combining main and auxiliary tasks into one pipeline.

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Apoorva Upadhyaya, Marco Fisichella, Wolfgang Nejdl

“We will Reduce Taxes” - Identifying Election Pledges with Language Models (2021.findings-acl)

Challenge:	Existing studies that focus on stance detection ignore the speech act, toxic, and moral features of tweets or lack an efficient architecture to detect the attitudes across targets.
Approach:	They propose a multitasking model that extracts valence, arousal, and dominance aspects hidden in tweets and injects the emotional sense into the embedded text followed by an efficient attention framework to correctly detect the tweet’s stance.
Outcome:	The proposed model exploits the toxicity, morality, and speech act features of the tweets to detect the public's stance.

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Tommaso Fornaciari, Dirk Hovy, Elin Naurin, Julia Runeson, Robert Thomson, Pankaj Adhikari

W-RST: Towards a Weighted RST-style Discourse Framework (2021.acl-long)

Challenge:	a political party's manifestos are published before any election, but do they follow through? a new study uses neural models to distinguish between actual pledges and general statements .
Approach:	They use election manifestos of Swedish and Indian political parties to learn neural models that distinguish actual pledges from generic positions.
Outcome:	The proposed model can predict election year and manifesto's party, while context introduces noise.

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Patrick Huber, Wen Xiao, Giuseppe Carenini

Challenge:	We show that weighted discourse trees from auxiliary tasks can benefit downstream applications . linguistic theories play a less and less critical role in the field of discourse .
Approach:	They propose a weighted-RST framework that assigns a binary assessment of importance between text segments by a relation attribute.
Outcome:	The proposed framework can be replaced by real-valued scores, the authors show . they show that weighted discourse trees can benefit key NLP downstream applications .

Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach (2024.findings-emnlp)

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Dongyue Li, Ziniu Zhang, Lu Wang, Hongyang Zhang

MultiOpEd: A Corpus of Multi-Perspective News Editorials (2021.naacl-main)

Challenge:	a new algorithm to estimate fine-tuning performance for a target task is proposed . conventional subset selection methods require repeated training on subsets of auxiliary tasks .
Approach:	They propose an algorithm to fine-tune a language model for a target task by optimally using auxiliary tasks' information.
Outcome:	The proposed method can estimate fine-tuning performance on CPUs in seconds.

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Siyi Liu, Sihao Chen, Xander Uyttendaele, Dan Roth

Challenge:	a news editorial is a genre of persuasive text where argumentation structure is usually implicit.
Approach:	They propose an open-domain news editorial corpus that supports automatic perspective discovery by identifying and abstracting natural language perspectives from editorials.
Outcome:	The proposed system supports automatic perspective discovery tasks in news editorials.

AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning (N19-1)

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Han Guo, Ramakanth Pasunuru, Mohit Bansal

Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule (2023.emnlp-main)

Challenge:	Multi-task learning is an inductive transfer mechanism that leverages information from related tasks to improve the primary model's generalization performance.
Approach:	They propose a multitask learning pipeline that finds relevant auxiliary tasks and learns their mixing ratio.
Outcome:	The proposed model can find relevant auxiliary tasks and learn their mixing ratio . the proposed model achieves significant performance boosts on several primary tasks .

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Andrey Bout, Alexander Podolskiy, Sergey Nikolenko, Irina Piontkovskaya

A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit (2022.coling-1)

Challenge:	Recent research has focused on using synthetic data for grammatical error correction . lack of annotated training data hinders progress in the field .
Approach:	They propose auxiliary tasks that exploit alignment between original and corrected sentences . they propose a sequence-to-sequence problem and perform multi-task training .
Outcome:	The proposed auxiliary tasks outperform the best models with a BART-based model on 11B parameters.

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Jivnesh Sandhan, Ashish Gupta, Hrishikesh Terdalkar, Tushar Sandhan, Suvendu Samanta, Laxmidhar Behera, Pawan Goyal

Learning Task Sampling Policy for Multitask Learning (2021.findings-emnlp)

Challenge:	Previously, compounding is a problem of identifying semantic relations between components of a word.
Approach:	They propose a multi-task learning architecture which incorporates contextual information and enriches syntactic information using morphological tagging and dependency parsing as auxiliary tasks.
Outcome:	The proposed architecture shows 6.1 points accuracy and 7.7 points (F1-score) absolute gain in English and Marathi languages.

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Dhanasekar Sundararaman, Henry Tsai, Kuang-Huei Lee, Iulia Turc, Lawrence Carin

RTE-GMoE: A Model-agnostic Approach for Relation Triplet Extraction via Graph-based Mixture-of-Expert Mutual Learning (2025.emnlp-main)

Challenge:	Existing methods to train multi-task models with auxiliary tasks are limited by the number of combinations and the importance of each auxiliary task is not known a priori.
Approach:	They propose a search method that automatically assigns importance weights to auxiliary tasks to improve the target task quality.
Outcome:	The proposed method outperforms uniform sampling and the corresponding single-task baseline on XNLI and GLUE.

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Aziguli Wulamu, Kaiyuan Gong, Lyu Zhengyu, Yu Han, Zhihong Zhu, Bowen Xing

Intra-Event and Inter-Event Dependency-Aware Graph Network for Event Argument Extraction (2023.findings-emnlp)

Challenge:	Relation Triplet Extraction (RTE) is a fundamental while challenge task in knowledge acquisition.
Approach:	They propose a mutual learning framework for Relation Triplet Extraction to address this limitation.
Outcome:	The proposed framework improves on four state-of-the-art backbones and benchmarks.

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Hao Li, Yanan Cao, Yubing Ren, Fang Fang, Lanxue Zhang, Yingjie Li, Shi Wang

A Multi-Task Approach for Improving Biomedical Named Entity Recognition by Incorporating Multi-Granularity information (2021.findings-acl)

Challenge:	Existing models do not build dependency information among event argument roles . Existing methods do not learn the interactions between different roles based on event structure .
Approach:	They propose an intra-event and inter-e event dependency-aware graph network to model dependencies between roles . they use event structure as the fundamental unit to construct role dependencies within events .
Outcome:	The proposed model improves on the ACE05, RAMS, and WikiEvents datasets.

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Yiqi Tong, Yidong Chen, Xiaodong Shi

Addressing Asymmetry in Multilingual Neural Machine Translation with Fuzzy Task Clustering (2022.coling-1)

Challenge:	Neural named entity recognition (BioNER) methods require large amount of annotated data, while the annotating BioNER datasets are often difficult to obtain and small in scale due to the limitations of privacy, ethics and high degree of specialization.
Approach:	They propose a method that utilizes latent multi-granularity information in annotated bioNER datasets to alleviate the lack of training samples.
Outcome:	The proposed model improves over the BioBERT baseline and can get more than 3% improvement of F1score in low-resource scenarios.

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Qian Wang, Jiajun Zhang

Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks (2021.acl-long)

Challenge:	Existing clustering methods cannot handle asymmetric problem in multilingual NMT . existing models cannot handle the asymmetry problem since there are thousands of languages involved .
Approach:	They propose a fuzzy task clustering method to address the asymmetric problem in multilingual NMT by using task affinity as the clustering criterion.
Outcome:	The proposed method outperforms baselines for a multilingual model and the existing models.

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Jinghui Qin, Xiaodan Liang, Yining Hong, Jianheng Tang, Liang Lin

Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text Generation (2021.acl-long)

Challenge:	Existing solutions for math word problems lack explicit integration of math symbolic constraints, leading to unexplainable and unreasonable predictions.
Approach:	They propose a novel mathematical model that explicitly incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning.
Outcome:	The proposed solver incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning.

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Liang Li, Can Ma, Yinliang Yue, Dayong Hu

HTML: Hierarchical Topology Multi-task Learning for Semantic Parsing in Knowledge Base Question Answering (2025.findings-acl)

Challenge:	Experimental results show that our method not only has a good generalization but also outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering.
Approach:	They propose to build an entity graph from the input tables and introduce a reasoning module to perform reasoning on the graph.
Outcome:	The proposed method outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering.

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Aziguli Wulamu, Lyu Zhengyu, Kaiyuan Gong, Yu Han, Zewen Wang, Zhihong Zhu, Bowen Xing

Consecutive Question Generation via Dynamic Multitask Learning (2022.findings-emnlp)

Challenge:	Existing approaches struggle with mapping questions to precise logical forms . Existing frameworks struggle with complex mapping of questions to logical form .
Approach:	They propose a framework that leverages a hierarchical multi-task learning paradigm to enhance the performance of logical form generation.
Outcome:	The proposed framework outperforms supervised fine-tuning methods and training-free ones on large language models.

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Yunji Li, Sujian Li, Xing Shi

Question Generation

Challenge:	, . ; ) ()((); ()) .())((2): ""().
Approach:	They propose a sequential sequential question-answer generation task with auxiliary tasks generating other elements to generate logically related question-anchor pairs to understand a passage.
Outcome:	The proposed framework improves question generation significantly and benefit multiple related tasks.

JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation (2022.coling-1)

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Jingyi You, Dongyuan Li, Manabu Okumura, Kenji Suzuki

Visual Data in NLP

Challenge:	Existing methods rarely consider cross-modal alignment between textual and visual features and ignore disease tags as auxiliary for report generation.
Approach:	They propose a "Jointly learning framework for automated disease Prediction and radiology report Generation" the framework integrates cross-modal alignment between textual and visual features and disease tags to improve the quality of reports.
Outcome:	The proposed framework improves the quality of radiology reports by combining the main task and auxiliary tasks.

Enhancing Image-to-Text Generation in Radiology Reports through Cross-modal Multi-Task Learning (2024.lrec-main)

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Nurbanu Aksoy, Nishant Ravikumar, Serge Sharoff

Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution (2020.coling-main)

Challenge:	Image-to-text generation relies on independent models for image understanding and natural language generation, which often exhibit a semantic gap between visual and textual information.
Approach:	They propose a multi-task learning framework to leverage both visual and non-imaging data for generating radiology reports.
Outcome:	The proposed framework improves performance over single-task baselines across language generation metrics and mitigates overfitting in auxiliary tasks.

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Juntao Yu, Nafise Sadat Moosavi, Silviu Paun, Massimo Poesio

What to Fuse and How to Fuse: Exploring Emotion and Personality Fusion Strategies for Explainable Mental Disorder Detection (2023.findings-acl)

Challenge:	a limitation of coreference resolution models is the focus on single-antecedent anaphors.
Approach:	They propose a model for unrestricted resolution of split-antecedent anaphors with multiple antecedents . they use auxiliary corpora where split-antcedent ananaphor was annotated by crowd .
Outcome:	The proposed model significantly improves on a baseline enhanced by BERT embeddings on anaphoric reference corpus.

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Sourabh Zanwar, Xiaofei Li, Daniel Wiechmann, Yu Qiao, Elma Kerz

Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (2020.emnlp-main)

Challenge:	Mental health disorders (MHD) are one of the greatest challenges facing our healthcare systems and modern societies in general.
Approach:	They integrate and extend the research by conducting extensive experiments with three types of deep learning-based fusion strategies: feature-level fusion, model fusion and task fusion.
Outcome:	The proposed techniques show that they can be used to improve mental health detection from textual data.

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Chenggong Gong, Jianfei Yu, Rui Xia

Sentiment Analysis

Challenge:	Existing approaches to aspect-based sentiment analysis rely on labeled data, but they lack the fine-grained labeles needed for the ABSA task.
Approach:	They propose a framework to perform feature adaptation and instance adaptation for the ABSA task . they learn domain-invariant feature representations by using part-of-speech features .
Outcome:	The proposed method improves on the state-of-the-art in two aspects of the ABSA task.

A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction (2025.coling-main)

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Xinpeng Liu, Bing Xu, Muyun Yang, Hailong Cao, Conghui Zhu, Tiejun Zhao, Wenpeng Lu

A Sentiment and Emotion Aware Multimodal Multiparty Humor Recognition in Multilingual Conversational Setting (2022.coling-1)

Challenge:	Existing approaches to address Grammatical Error Correction (GEC) tasks are based on large scale labeled data, which leads to extremely high data annotation costs.
Approach:	They propose a Chain-of-Task framework to reduce over-correction in large language models . they propose supervised fine-tuning strategy and an algorithm for automatic dataset annotation .
Outcome:	The proposed framework achieves state-of-the-art on both FCGEC (in-domain) and NaCGEC (out-of domain) test sets.

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Dushyant Singh Chauhan, Gopendra Vikram Singh, Aseem Arora, Asif Ekbal, Pushpak Bhattacharyya

Natural Language Interfaces

Challenge:	Humor is an essential aspect of daily conversation, and people try to provoke humor in their talks.
Approach:	They propose a multitask framework that annotates Hindi utterances with sentiment and emotion classes.
Outcome:	The proposed framework improves on the recently released Hindi Humor dataset . it takes sentiment and emotion into account to understand humor .

Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction (2022.coling-1)

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Shunjie Chen, Xiaochuan Shi, Jingye Li, Shengqiong Wu, Hao Fei, Fei Li, Donghong Ji

Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features (2024.findings-acl)

Challenge:	Existing methods for ECPE fail to model specific features and interactive features in between, or suffer from inconsistency of label prediction.
Approach:	They propose to align ECPE with a feature-task alignment mechanism to model emotion-&cause-specific features and the shared interactive feature.
Outcome:	The proposed model outperforms existing systems on all ECA subtasks.

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Mengyu Bu, Shuhao Gu, Yang Feng

Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning (2024.findings-acl)

Challenge:	Existing models do not differentiate between semantic and linguistic features, resulting in the entanglement of knowledge and linguistics within the model.
Approach:	They propose to exploit both semantic and linguistic features to enhance multilingual translation by disentangling encoder representations and integrating low-level linguistic encoders.
Outcome:	The proposed model improves zero-shot translation while maintaining performance in supervised translation on multilingual datasets.

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Xiaohu Du, Ming Wen, Jiahao Zhu, Zifan Xie, Bin Ji, Huijun Liu, Xuanhua Shi, Hai Jin

Rethinking and Improving Multi-task Learning for End-to-end Speech Translation (2023.emnlp-main)

Challenge:	Existing CodePre-trained models struggle to generalize due to superficial mapping from source code to labels instead of understanding the root causes of code vulnerabilities.
Approach:	They propose a framework that integrates multi-task learning with Large Language Models to effectively mine deep-seated vulnerability features.
Outcome:	The proposed framework surpasses seven state-of-the-art models in effectiveness, generalization, and robustness.

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Yuhao Zhang, Chen Xu, Bei Li, Hao Chen, Tong Xiao, Chunliang Zhang, Jingbo Zhu

Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach (2021.emnlp-main)

Challenge:	auxiliary tasks are highly consistent with end-to-end speech translation (ST) but their effectiveness has not been thoroughly studied.
Approach:	They propose an improved multi-task learning approach for the ST task that bridges the modal gap by mitigating the difference in length and representation.
Outcome:	The proposed approach achieves state-of-the-art on the MuST-C dataset with 20.8% of training time required by the current SOTA method.

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Víctor M. Sánchez-Cartagena, Miquel Esplà-Gomis, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez

DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Task (2023.findings-emnlp)

Challenge:	Existing approaches to generating additional parallel sentences are aimed at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words.
Approach:	They propose to use data augmentation techniques to generate additional parallel sentences by reversing the order of the target sentence to produce unfluent target sentences.
Outcome:	The proposed approach improves on six low-resource translation tasks and the baseline and over DA methods.

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Guanting Dong, Tingfeng Hui, Zhuoma GongQue, Jinxu Zhao, Daichi Guo, Gang Zhao, Keqing He, Weiran Xu

A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check (2023.findings-emnlp)

Challenge:	Recent prompt-based generative frameworks have shown impressive capabilities in sequence labeling tasks.
Approach:	They propose a multi-task demonstration-based generative framework for noisy slot filling that captures input perturbations at different granularities.
Outcome:	The proposed framework outperforms baseline methods and achieves strong generalization.

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Haojing Huang, Jingheng Ye, Qingyu Zhou, Yinghui Li, Yangning Li, Feng Zhou, Hai-Tao Zheng

Reasoning

Challenge:	Recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks.
Approach:	They propose to decompose Chinese Spelling Check into detection, reasoning, and searching subtasks and to train a module that is compatible with existing CSC models.
Outcome:	The proposed module can be trained for one model and benefit other models.

TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition (2023.findings-acl)

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Wei Xiang, Chao Liang, Bang Wang

LOHRec: Leveraging Order and Hierarchy in Generative Sequential Recommendation (2025.findings-emnlp)

Challenge:	Existing prompt learning models for IDRR use multiple-prompt decisions from three different yet much similar connective prediction templates.
Approach:	They propose to fuse three related tasks to fuse the learned features of auxiliary tasks to create a prompt learning model that can be used to boost the main task.
Outcome:	The proposed model outperforms the ConnPrompt in the training phase and in the testing phase.

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Jiawen Xie, Haiyang Wu, Deyi Ji, Yuekui Yang, Shaoping Ma

Challenge:	generative recommenders focus on maximizing the prediction probability of the next item in the temporal sequence, ignoring diverse potential items.
Approach:	They propose a learning framework that leverages order and hierarchy in generative recommendation using quantized identifiers to further explore performance ceiling of lightweight generative recommenders.
Outcome:	The proposed learning framework outperforms strong prior baselines across multiple datasets.

Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification (2023.emnlp-main)

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Apoorva Singh, Siddarth Chandrasekar, Sriparna Saha, Tanmay Sen