Papers with multi-task framework
Incorporating Fine-grained Events in Stock Movement Prediction (D19-51)
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| Challenge: | Existing studies mainly adopt coarse-grained events, which loses the specific semantic information of diverse event types. |
| Approach: | They propose to use a finance event dictionary to extract fine-grained events from finance news to train a neural model that uses the extracted events as the distant supervised label to train stock prediction. |
| Outcome: | The proposed method outperforms baselines and has good generalizability. |
N-LTP: An Open-source Neural Language Technology Platform for Chinese (2021.emnlp-demo)
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| Challenge: | Existing tools that teach an independent model for each task are not supported in Chinese. |
| Approach: | They propose an open-source neural language platform supporting six Chinese NLP tasks . source code, documentation, and pre-trained models are available at https://github.com/hit-SCIR/ltp . |
| Outcome: | The proposed platform supports six Chinese NLP tasks. |
A Unified Framework for Emotion Identification and Generation in Dialogues (2023.eacl-srw)
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| Challenge: | Social chatbots have gained immense popularity and can be used to develop and promote social chatbot applications. |
| Approach: | They propose a multi-task framework that jointly identifies the emotion of a given dialogue and generates response in accordance to the identified emotion. |
| Outcome: | The proposed framework outperforms current state-of-the-art models with classification and generation loss. |
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes (2020.aacl-main)
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| Challenge: | Empirical results show the efficacy of our proposed multi-task framework over existing state-of-the-art systems. |
| Approach: | They propose a multi-task, multi-modal deep learning framework to solve multiple tasks simultaneously. |
| Outcome: | The proposed framework performs better than existing state-of-the-art systems on a complicated form of information, i.e., memes. |
Constrained Multi-Task Learning for Bridging Resolution (2022.acl-long)
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| Challenge: | bridging resolution is the task of recognizing and resolving bridling anaphors in a text. |
| Approach: | They propose a constrained multi-task learning framework for bridging resolution that exploits cross-task consistency constraints to guide the learning process and pre-train the entity coreference model on publicly available coreference data. |
| Outcome: | The proposed model achieves state-of-the-art on three standard evaluation corpora. |
Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings (C18-1)
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| Challenge: | Existing approaches focus on text information, but authors and overall ratings are ignored, both of which are proved to be significant on interpreting the sentiments of different aspects. |
| Approach: | They propose a hierarchical user-aspect rating network model to consider user preference and overall ratings jointly. |
| Outcome: | The proposed model can predict aspects of a product in two real-world datasets. |
Entity-level Cross-modal Learning Improves Multi-modal Machine Translation (2021.findings-emnlp)
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| Challenge: | Multi-modal machine translation aims at improving translation performance by incorporating visual information. |
| Approach: | They propose an explicit entity-level cross-modal learning approach that aims to augment the entity representation by combining a translation task and a reconstruction task. |
| Outcome: | The proposed approach achieves comparable or even better performance than state-of-the-art models. |
Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization (C18-1)
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| Challenge: | Existing approaches focus on improving the informativeness of the summary, but ignore the correctness. |
| Approach: | They propose an entailment-aware encoder and an aML-based decoder to improve the correctness of the sentence summarization task. |
| Outcome: | The proposed model outperforms baselines on informativeness and correctness. |
Chinese Live-Streaming E-Commerce Morph Resolution: Datasets and Methods (2026.findings-acl)
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| Challenge: | Live-stream E-commerce faces significant challenges from morphs, deliberate linguistic variants used to evade real-time voice filters and amplify product claims illegally. |
| Approach: | They propose a framework that resolves morphs and generates structured explanations . they propose morph-aware dual-output refinement framework that detects inconsistencies . |
| Outcome: | The proposed framework improves morph resolution accuracy and interpretability. |
Transfer Learning and Prediction Consistency for Detecting Offensive Spans of Text (2022.findings-acl)
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| Challenge: | Existing models for toxic span detection only classify text snippets as offensive or not . a novel model seeks to simultaneously predict offensive words and opinion phrases . |
| Approach: | They propose a novel model that seeks to predict offensive words and opinion phrases simultaneously . they also introduce a regularization mechanism to encourage consistency of the model predictions . |
| Outcome: | The proposed model performs well compared to baselines on toxic span detection tasks . it predicts offensive words and opinion phrases to leverage inter-dependencies . |
Why Don’t You Do It Right? Analysing Annotators’ Disagreement in Subjective Tasks (2023.eacl-main)
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| Challenge: | Disagreement can reflect different aspects of linguistic annotation, from annotators’ subjectivity to sloppiness or lack of context to interpret a text. |
| Approach: | They propose a taxonomy of possible reasons leading to annotators' disagreement in subjective tasks and manually label part of a Twitter dataset for offensive language detection in english following this taxonomies. |
| Outcome: | The proposed taxonomy of disagreements in linguistic datasets can be used to assess how accurate tweets belonging to different disagreement categories can be classified as offensive or not. |
Generate & Rank: A Multi-task Framework for Math Word Problems (2021.findings-emnlp)
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| Challenge: | Existing studies formalize MWP as a generation task but mathematical expressions are prone to minor mistakes. |
| Approach: | They propose a ranking task for math word problem (MWP) that learns from its own mistakes and distinguishes between correct and incorrect expressions. |
| Outcome: | The proposed model outperforms baselines on the classical Math23k dataset and is 7% higher than the state-of-the-art. |
CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems (2021.naacl-main)
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| Challenge: | Existing systems that negotiate with humans have broad applications in pedagogy and conversational AI. |
| Approach: | They propose to annotate persuasion strategies and perform correlation analysis to understand how dialogue behaviors are associated with the negotiation performance. |
| Outcome: | The proposed system improves negotiation performance for all strategies labeled as skewed . the proposed system is available on github.com/kushalchawla/ . |
A Multi-Persona Chatbot for Hotline Counselor Training (2020.findings-emnlp)
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| Challenge: | a chatbot cannot replace a counselor, but a simulation of intimate situations is needed to train counselors. |
| Approach: | They propose a counseling strategy annotation scheme and a multi-task framework that mimics prototype conversations to train counselors. |
| Outcome: | The proposed framework significantly increases response diversity and specificity, with limited impact to coherence. |
RA-MTR: A Retrieval Augmented Multi-Task Reader based Approach for Inspirational Quote Extraction from Long Documents (2025.coling-main)
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| Challenge: | Inspirational quotes from famous individuals are powerful tools that convey wisdom and insight in a concise and often figurative manner. |
| Approach: | They propose a context-based quote extraction system that aims to predict the most relevant quote from a long text. |
| Outcome: | The proposed system improves on a dataset with 5.08% BoW F1-score. |
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. |
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis (2020.acl-main)
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| Challenge: | Existing systems for sarcasm detection are limited by the use of sarcasm . sarasm is often used to convey thinly veiled disapproval humorously. |
| Approach: | They propose a multi-task deep learning framework to solve sarcasm problems simultaneously . they manually annotate a sarcsm dataset with sentiment and emotion classes . |
| Outcome: | The proposed framework is able to solve sarcasm, sentiment and emotion problems in a multi-modal conversational scenario. |
Joint Intent Detection and Entity Linking on Spatial Domain Queries (2020.findings-emnlp)
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| Challenge: | Spatial domain queries have unique properties making them more challenging for language understanding than common conversational queries. |
| Approach: | They propose a language understanding framework for spatial domain queries that jointly learns the intent detection and entity linking tasks on a voice assistant service. |
| Outcome: | The proposed framework outperforms baseline methods with a significant margin. |
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. |
Cross-lingual Social Misinformation Detector based on Hierarchical Mixture-of-Experts Adapter (2025.coling-main)
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| Challenge: | a global trend of misinformation is affecting non-native speaker users who are more susceptible to misinformation on foreign social media platforms. |
| Approach: | They propose a method to integrate sentiment analysis as an auxiliary task and a hierarchical routing strategy and expert-mask mechanism to enhance cross-lingual social misinformation detection. |
| Outcome: | The proposed method improves cross-lingual social misinformation detection in non-native speakers with only monolingual social media histories. |
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes (2023.findings-acl)
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Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister
| Challenge: | Deploying large language models (LLMs) is difficult because they are memory inefficient and compute-intensive for practical applications. |
| Approach: | They propose a mechanism that fine tunes or distills small models that outperform LLMs . they use human labels to fine tune models or LLM-generated labels to train models . |
| Outcome: | The proposed method outperforms LLMs by using fewer training examples compared to few-shot prompted models using substantially smaller model sizes. |
A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature (D19-1)
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| Challenge: | Existing academic search engines cannot detect relevant papers where a resource is mentioned. |
| Approach: | They propose a framework to model the role and function of on-line resource citations . they construct a dataset SciRes, which includes 3,088 manually annotated resource contexts based on a multi-task framework . |
| Outcome: | The proposed model achieves the best results on both the classification task and recommendation task. |
Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference (P19-1)
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| Challenge: | Existing models for SLU use explicit memory representations, but the context memory is under-exploited. |
| Approach: | They propose a dialogue logistic inference task to consolidate the context memory with SLU in a multi-task framework. |
| Outcome: | The proposed model improves slot filling and domain classification performance in a multi-task framework. |
What to Fuse and How to Fuse: Exploring Emotion and Personality Fusion Strategies for Explainable Mental Disorder Detection (2023.findings-acl)
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| 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. |
APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning (2020.emnlp-main)
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| Challenge: | Argument mining is an important research field that attracts growing attention in recent years. |
| Approach: | They propose a new task to extract argument pairs from peer review and rebuttal . they use an open review platform to analyze the contents, structure and connections . |
| Outcome: | The proposed task is based on a dataset of 4,764 fully annotated review-rebuttal passage pairs . it is able to detect argumentative propositions and extract argument pairs from the corpus . |
Entity, Relation, and Event Extraction with Contextualized Span Representations (D19-1)
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| Challenge: | Existing frameworks for named entity recognition, relation extraction, and event extraction can be easily adapted for new tasks or datasets. |
| Approach: | They propose a framework that enumerates, refins, and scores text spans to capture local (within-sentence) and global (cross-sentent) context. |
| Outcome: | The proposed framework achieves state-of-the-art results on four datasets from a variety of domains. |
Multi-Task Stance Detection with Sentiment and Stance Lexicons (D19-1)
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| Challenge: | Recent studies show improvements in stance detection by using attention mechanism or sentiment information. |
| Approach: | They propose a multi-task framework that incorporates attention mechanism and takes sentiment classification as an auxiliary task. |
| Outcome: | The proposed model outperforms state-of-the-art deep learning methods on the SemEval-2016 dataset. |
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach (2021.emnlp-main)
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| 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. |