Papers with multi-task training
Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection (2024.findings-eacl)
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| Challenge: | Event detection is a crucial information extraction task in many domains, such as Wikipedia or news. |
| Approach: | They propose to use subject-object relations obtained from a rule-based open information extraction system to enhance domain transfer of trigger detection (TD) they combine this enhanced transfer with masked language modeling on the target domain, observing further TD transfer gains. |
| Outcome: | The proposed model improves the transfer of triggers between domains and reduces performance drops when using a low-resource source domain to a high-res target domain. |
Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning (2024.findings-emnlp)
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Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Kumar Dubey, Alexandre Rame, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Leonard Hussenot, Olivier Bachem, Edouard Leurent
| Challenge: | Existing approaches for multi-objective Reinforcement Learning (RL) are difficult due to plurality of preferences and applications. |
| Approach: | They propose a framework for finetuning language models on multiple objectives using conditional language policy. |
| Outcome: | The proposed framework outperforms and Pareto-dominates existing approaches for multi-objective Reinforcement Learning (RL) it does not require training or maintaining multiple models to achieve different trade-offs between the objectives. |
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning (2022.naacl-main)
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| Challenge: | Recent work has found that multi-task training with a large number of diverse tasks can uniformly improve downstream performance on unseen target tasks. |
| Approach: | They aim to disentangle the effect of scale and relatedness of tasks in multi-task representation learning by increasing the number of tasks and incorporating smaller sets of related tasks. |
| Outcome: | The proposed model improves on unseen target tasks by increasing the scale of multi-task learning to incorporate more tasks and developing similarity metrics to incorporate tasks related to the target task. |
Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills (2020.acl-main)
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| Challenge: | Existing work has focused on learning specific qualities of conversational agents, but it remains unclear how to combine them. |
| Approach: | They propose to combine models trained towards isolated capabilities with multi-task training to improve conversation performance. |
| Outcome: | The proposed dataset compares models trained towards isolated capabilities with models trained on a single skill. |
VIBE: Topic-Driven Temporal Adaptation for Twitter Classification (2023.emnlp-main)
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| Challenge: | Language features are evolving in real-world social media, resulting in deteriorating performance of text classification. |
| Approach: | They propose a model that allows models to adapt to shifted data via latent topic evolution . they use two information bottleneck regularizers to distinguish past and future topics . |
| Outcome: | The proposed model outperforms state-of-the-art models on Twitter on three tasks with 3% of data. |
Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining (2020.emnlp-main)
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| Challenge: | Pre-trained neural language models improve learning for various NLP tasks by fine-tuning them on task-specific training sets. |
| Approach: | They propose a meta-learning procedure to fine-tune neural language models on task-specific training sets. |
| Outcome: | The proposed procedure solves a group of similar NLP tasks on a text mining dataset. |
WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)
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Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei
| Challenge: | Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions. |
| Approach: | They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach . |
| Outcome: | The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size. |
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media (2021.findings-emnlp)
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| Challenge: | Adverse Events (AEs) are harmful events resulting from the use of medical products. |
| Approach: | They propose a model that combines sequence-to-sequence learning with language transfer capabilities to improve model robustness. |
| Outcome: | The proposed approach achieves strong performance over baselines on English benchmarks. |
LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization (2022.coling-1)
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| Challenge: | Existing work has addressed each element individually, but this study focuses on LipKey, the largest news corpus with human-written abstractive summaries, absent keyphrases, and titles. |
| Approach: | They propose a novel news dataset that consists of highly absent keyphrases . they combine lips keyphrase and TF-IDF to obtain abstractive summaries . |
| Outcome: | The proposed dataset is the largest news corpus with human-written abstractive summaries, absent keyphrases, and titles. |
Multimodal Chart Retrieval: A Comparison of Text, Table and Image Based Approaches (2024.naacl-long)
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| Challenge: | Multimodal chart retrieval is a task of retrieving information from a multimodal dataset, given a query. |
| Approach: | They compare four different approaches to multimodal chart retrieval . they use chart derendering, chart derending, table retrieval and Tab-GTR . |
| Outcome: | The proposed model outperforms the previous methods on the NQ-Tables benchmark and is significantly more efficient on in-distribution data. |
Distilling Knowledge for Empathy Detection (2021.findings-emnlp)
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| Challenge: | Empathy is the link between self and others. |
| Approach: | They employ multi-task training with knowledge distillation to integrate knowledge from available resources to detect empathy from the natural language in different domains. |
| Outcome: | The proposed approach yields better results on an existing news-related empathy dataset compared to strong baselines. |
DivMerge: A divergence-based model merging method for multi-tasking (2026.eacl-long)
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| Challenge: | Existing methods for multitask learning struggle with interference between tasks, especially as the number of tasks grows. |
| Approach: | They propose a reference-free method that minimizes the divergence between models' outputs and those of the merged model, automatically balancing task importance. |
| Outcome: | The proposed method outperforms existing methods on classification and generative tasks and remains robust when scaling to more tasks. |
SciRepEval: A Multi-Format Benchmark for Scientific Document Representations (2023.emnlp-main)
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| Challenge: | Existing benchmarks for evaluating scientific document representations fail to capture the diversity of relevant tasks. |
| Approach: | They propose a benchmark for training and evaluating scientific document representations that includes 24 challenging and realistic tasks across four formats: classification, regression, ranking and search. |
| Outcome: | The proposed model outperforms existing models by over 2 points absolute. |
Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule (2023.emnlp-main)
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| 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. |
Enhancing Language Model Hypernetworks with Restart: A Study on Optimization (2025.naacl-long)
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| Challenge: | a comprehensive investigation into optimization strategies for hypernetworks remains lacking. |
| Approach: | They propose restart optimization strategies to improve hypernetworks' performance for language models. |
| Outcome: | The proposed restart strategy improves hypernetworks' performance for language models, compared to conventional deep neural networks. |
Lemmatisation & Morphological Analysis of Unedited Greek: Do Simple Tasks Need Complex Solutions? (2025.findings-acl)
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| Challenge: | Existing systems for part-of-speech tagging of unedited Greek text have outperformed traditional methods for morphological analysis and lemmatisation. |
| Approach: | They propose to combine nominal features into a single label and combine the three most distinctive features of verbs into another unified label. |
| Outcome: | The proposed models outperform traditional models in lemmatisation and morphological analysis and show that multi-task learning improves performance by transferring parameters. |
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts (2022.emnlp-main)
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| Challenge: | a new multi-task, parameter-efficient language model tuning method learns to transfer knowledge across different tasks via a mixture of soft prompts. |
| Approach: | They propose a multi-task, parameter-efficient language model tuning method that uses soft prompts to learn to transfer knowledge across different tasks. |
| Outcome: | The proposed method outperforms prompt tuning and outperfies or matches fully fine-tuned tuning approaches that use 10 times more parameters. |
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks (2020.coling-main)
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| Challenge: | Pre-trained transformer models have shown great success in improving performance on downstream tasks, but fine-tuning on a new task still requires large amounts of labeled data. |
| Approach: | They propose a method which allows optimization-based meta-learning across tasks . they use transformers to train transformer models and find better generalizations . |
| Outcome: | The proposed method outperforms self-supervised training and pre-trained models on 17 NLP tasks. |
Jointly Learning to Align and Translate with Transformer Models (D19-1)
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| Challenge: | Existing word alignment models are not accurate for word alignments. |
| Approach: | They propose a method to train a Transformer model to produce accurate translations and alignments. |
| Outcome: | The proposed model outperforms GIZA++ trained models on translation and alignment tasks while maintaining translation accuracy. |
UniTE: Unified Translation Evaluation (2022.acl-long)
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| Challenge: | Recent methods for evaluation of translation quality are focused on one task, ignoring commonalities . |
| Approach: | They propose a unified framework engaged with abilities to handle all three evaluation tasks. |
| Outcome: | The proposed framework can universally surpass state-of-the-art or winner methods across tasks. |
BAM! Born-Again Multi-Task Networks for Natural Language Understanding (P19-1)
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| Challenge: | Existing methods to train multi-task neural networks outperform or even match their single-task counterparts are difficult to implement. |
| Approach: | They propose a method that uses knowledge distillation to train multi-task neural networks that outperform or even match their single-task counterparts. |
| Outcome: | The proposed method outperforms or matches single-task neural networks on the GLUE benchmark. |
Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging (2025.acl-long)
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Zhenyang Cai, Junying Chen, Rongsheng Wang, Weihong Wang, Yonglin Deng, Dingjie Song, Yize Chen, Zixu Zhang, Benyou Wang
| Challenge: | Current research suggests that multitask training outperforms single-task as different tasks can benefit each other, but they often overlook the internal relationships within these tasks. |
| Approach: | They employ compositional generalization (CG) to examine the generalization of multimodal large language models in medical imaging. |
| Outcome: | The proposed model can understand unseen medical images and is able to perform CG across classification and detection tasks. |
What’s in Your Head? Emergent Behaviour in Multi-Task Transformer Models (2021.emnlp-main)
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| Challenge: | Existing paradigms for multi-task training involve a shared pre-trained language model and a small, thin network (head) given an input, a target head is the head that is selected for outputting the final prediction. |
| Approach: | They examine the behaviour of non-target heads when given input that belongs to a different task than the one they were trained for. |
| Outcome: | The non-target heads exhibit emergent behaviour, which may explain the target task, or generalize beyond their original task. |
MARE: Multi-Aspect Rationale Extractor on Unsupervised Rationale Extraction (2024.emnlp-main)
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| Challenge: | Existing methods to extract text snippets from input text to support model predictions without explicit rationale annotation have limited their ability to capture meaningful internal correlations between aspects. |
| Approach: | They propose a multi-aspect rationale extractor that extracts text snippets to support model predictions without explicit rationale annotation. |
| Outcome: | The proposed method achieves state-of-the-art on two unsupervised rationale extraction benchmarks. |
TaskWeb: Selecting Better Source Tasks for Multi-task NLP (2023.emnlp-main)
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| Challenge: | Recent work in NLP has shown that knowing task relationships via pairwise task transfer improves choosing one or more source tasks that help to learn a new target task. |
| Approach: | They propose a method to quantify task relationships via pairwise task transfer and build smaller training sets that improve zero-shot performances across 11 different target tasks. |
| Outcome: | The proposed method improves overall rankings and top-k precision of source tasks by 10% and 38%, respectively. |
Structural Information Preserving for Graph-to-Text Generation (2020.acl-main)
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| Challenge: | Existing models that mess up or drop the core structural information of input graphs are lacking in graph-to-text generation. |
| Approach: | They propose to leverage richer training signals to guide a graph-to-text generation model by focusing on autoencoding losses and back-propagating the losses to better calibrate the model. |
| Outcome: | Experiments on two benchmarks show the proposed model over a state-of-the-art model . two types of autoencoding losses are used to back-propagate the model based on multitask training . |
Concept-aware Data Construction Improves In-context Learning of Language Models (2024.findings-acl)
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| Challenge: | Recent work curating in-context learners assumes that ICL emerges from vast over-parametrization or the scale of multitask training. |
| Approach: | They propose a framework for constructing training scenarios that make it beneficial for the LM to learn to utilize the analogical reasoning concepts from demonstrations. |
| Outcome: | The proposed framework makes it beneficial for the LM to learn to utilize the analogical reasoning concepts from demonstrations and fares comparably to previous in-context learners trained in large-scale multitask learning requiring magnitudes of more training data. |
TAMA: Target-Aware Multilingual Abuse Detection by Cascaded Conditional Multi-Task Learning (2026.acl-long)
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| Challenge: | Existing models for protecting public figures from online abuse ignore who is targeted and how. |
| Approach: | They propose a target-aware multi-task framework that conditions downstream predictions on upstream beliefs via three lightweight modules: Cross-Task Feature Fusion (CTF), Task-Adaptive Gating (TAG), and Label-Guided Span Detection (LGSD). |
| Outcome: | The proposed framework yields higher average F1 than single-task training and standard multi-task learning. |
MaNtLE: Model-agnostic Natural Language Explainer (2023.emnlp-main)
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| Challenge: | Recent research suggests that practitioners prefer examining language explanations that explain sub-groups of examples. |
| Approach: | They propose a model-agnostic natural language explainer that generates faithful explanations of classifier rationale for structured classification tasks. |
| Outcome: | The proposed model-agnostic natural language explainer generates faithful explanations of classifier rationale for structured classification tasks. |
Multi-Task Transfer Matters During Instruction-Tuning (2024.findings-acl)
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| Challenge: | Instruction-tuning improves a model’s ability to learn in-context, but the mechanisms that drive in-constext learning are poorly understood. |
| Approach: | They propose to train a model on hundreds of tasks to improve its ability to learn in-context. |
| Outcome: | The proposed methods improve model transfer and in-context generalization, suggesting catastrophic forgetting may impact in-constext learning. |
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning (2023.acl-long)
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Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Yang Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang
| Challenge: | Existing methods for data-to-text generation focus on specific types of structured data. |
| Approach: | They propose a method that provides a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations. |
| Outcome: | The proposed method improves zero-shot and few-shot scenarios and can adapt to new structured data. |
mForms : Multimodal Form Filling with Question Answering (2024.lrec-main)
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| Challenge: | The paper presents a new approach to form-filling by reformulating the task as multimodal natural language Question Answering (QA). |
| Approach: | They propose a new approach to form-filling by reformulating the task as multimodal natural language Question Answering (QA) the paper introduces a multimodal form-filled dataset and an extension of the popular ATIS dataset to support future research and experimentation. |
| Outcome: | The proposed approach maintains robust accuracy for sparse training conditions and achieves state-of-the-art F1 of 0.97 on ATIS with approximately 1/10th the training data. |
Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning (2023.emnlp-main)
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| Challenge: | Experimental results show that multi-head attention module evolves functional specialization after multi-task training. |
| Approach: | They propose a method to quantify the degree of functional specialization in multi-head attention . they propose 'multi-task training' method to increase functional specialisation and mitigate negative information transfer . |
| Outcome: | The proposed method increases functional specialization and mitigates negative information transfer in multi-task learning without adding any parameters. |
SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation (2026.acl-long)
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Mahi Luthra, Jiayi Shen, Maxime Poli, Angelo Ortiz Tandazo, Yosuke Higuchi, Youssef Benchekroun, Martin Gleize, Charles-Éric Saint-James, Dongyan Lin, Phillip Rust, Angel Villar-Corrales, null Surya, Vanessa Stark, Rashel Moritz, Juan Pino, Yann LeCun, Emmanuel Dupoux
| Challenge: | Empirically, SpidR-Adapt achieves rapid gains in phonemic discriminability and downstream spoken language modeling scores . current self-supervised learning models require thousands of hours of training data to learn meaningful linguistic representations. |
| Approach: | They propose a bi-level optimization framework for rapid adaptation of speech units to new languages using minimal unlabeled data. |
| Outcome: | The proposed model achieves rapid gains in phonemic discriminability and spoken language modeling scores . it surpasses in-domain toplines after training on less than 1h of target-language audio . |
tasksource: A Large Collection of NLP tasks with a Structured Dataset Preprocessing Framework (2024.lrec-main)
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| Challenge: | Several initiatives release harmonized datasets or provide harmonization codes to preprocess datasets into a consistent format. |
| Approach: | They propose an annotation framework that enables concise, readable, and reusable annotations. |
| Outcome: | The proposed framework outperforms all publicly available text encoders on all tasks. |