Papers with data-driven approaches
Deep Learning Approaches to Text Production (N18-6)
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| Challenge: | Text production is a key component of many NLP applications . Claire Gardent is based in France and is pursuing research in text production . |
| Approach: | This tutorial will cover the fundamentals and state-of-the-art research on neural models for text production. |
| Outcome: | This tutorial will cover the fundamentals and the state-of-the-art research on neural models for text production. |
Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition (2021.acl-short)
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| Challenge: | Existing work on humor recognition does not examine the actual joke mechanism . a recent study focused on humor-specific stylistic features, but few have tried to establish a connection between them and humor theories. |
| Approach: | They propose to model the set-up and punchline as part developing semantic uncertainty and disrupt audience expectations. |
| Outcome: | The proposed features can tell jokes from non-jokes, compared with baselines. |
Literature Meets Data: A Synergistic Approach to Hypothesis Generation (2025.acl-long)
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| Challenge: | Existing methods for hypothesis generation are theory-driven and data-driven, but they lack the computational power to complement each other. |
| Approach: | They develop a method that combines literature-based insights with data to perform LLM-powered hypothesis generation. |
| Outcome: | The proposed method outperforms baseline methods on five datasets and shows human accuracy improves on deception detection and AI generated content detection tasks. |
LLM Questionnaire Completion for Automatic Psychiatric Assessment (2024.findings-emnlp)
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| Challenge: | Psychiatric evaluations are heavily based on patient verbal reports of disturbed feelings, thoughts, behaviors, and their changes over time. |
| Approach: | They employ a Large Language Model to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. |
| Outcome: | The proposed model improves diagnostic accuracy compared to baselines. |
Marrying LLMs with Dynamic Forecasting: A Graph Mixture-of-expert Perspective (2025.findings-naacl)
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| Challenge: | Recent data-driven approaches often use graph neural networks (GNNs) to learn relationships in dynamical systems. |
| Approach: | They propose a framework which leverages large language models to enhance generalization capabilities of dynamical system modeling. |
| Outcome: | The proposed framework improves on existing methods and compares to baselines. |
Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction (2022.findings-emnlp)
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Shirong Ma, Yinghui Li, Rongyi Sun, Qingyu Zhou, Shulin Huang, Ding Zhang, Li Yangning, Ruiyang Liu, Zhongli Li, Yunbo Cao, Haitao Zheng, Ying Shen
| Challenge: | Chinese Grammatical Error Correction (CGEC) is a challenging NLP task and a common application in human daily life. |
| Approach: | They propose a linguistic rules-based approach to construct large-scale CGEC training corpora with automatically generated grammatical errors. |
| Outcome: | The proposed method improves performance of existing CGEC models and the benchmark is excellent resource for further development. |
MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection (2025.acl-long)
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| Challenge: | a rapid expansion of memes on social media highlights the need for effective methods to detect harmful content. |
| Approach: | They propose a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data. |
| Outcome: | The proposed framework outperforms existing zero-shot approaches on three meme datasets. |
Cross-Lingual Learning-to-Rank with Shared Representations (N18-2)
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| Challenge: | Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user's query. |
| Approach: | They propose a large-scale dataset derived from Wikipedia to support CLIR research in 25 languages. |
| Outcome: | The proposed model can improve the results of Swahili-English CLIR in Japanese and Japanese. |
Learning to Answer Psychological Questionnaire for Personality Detection (2021.findings-emnlp)
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| Challenge: | Existing text-based personality detection research relies on data-driven approaches to implicitly capture personality cues in online posts lacking the guidance of psychological knowledge. |
| Approach: | They propose a model to capture key information in texts and a questionnaire to help the user to make a personality assessment. |
| Outcome: | The proposed model captures key information in texts and a questionnaire and can be used to improve personality prediction. |
Multi-label and Multi-target Sampling of Machine Annotation for Computational Stance Detection (2023.findings-emnlp)
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| Challenge: | Existing methods for data-driven annotations require domain-specific and task-aligned supervision. |
| Approach: | They propose a multi-label and multi-target sampling strategy to optimize the annotation quality. |
| Outcome: | The proposed method significantly improves performance and learning efficacy on the benchmark stance detection corpora. |
A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd (N19-1)
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| Challenge: | Existing methods for analyzing discourse-level argument annotations require expensive labor and data. |
| Approach: | They propose a method that breaks down a popular but complex discourse-level argument annotation scheme into a simple iterative procedure that can be applied even by untrained annotators. |
| Outcome: | The proposed method can be applied even by untrained annotators. |
Guiding Computational Stance Detection with Expanded Stance Triangle Framework (2023.acl-long)
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| Challenge: | Experimental results show that strategically-enriched data can significantly improve the performance on out-of-domain and cross-target evaluation. |
| Approach: | They propose to decompose a stance detection task from a theoretical perspective and extend it with additional annotations. |
| Outcome: | The proposed task improves performance on out-of-domain and cross-target evaluations using a linguistic framework. |
An Ordinal Latent Variable Model of Conflict Intensity (2023.acl-long)
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| Challenge: | Advances in automated event extraction yield massive data sets of “who did what to whom” micro-records that enable data-driven approaches to monitoring conflict. |
| Approach: | They propose a probabilistic generative model that assumes each observed event is associated with a latent intensity class. |
| Outcome: | The proposed model obtains comparatively good held-out predictive performance on a conflictual to cooperative scale. |
On the Limitations of Simulating Active Learning (2023.findings-acl)
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| Challenge: | Active learning (AL) is a human-and-model-in-the-loop paradigm that iteratively selects informative unlabeled data for human annotation. |
| Approach: | They propose to simulate active learning by using an already labeled dataset as the pool of unlabeled data. |
| Outcome: | The proposed model-in-the-loop paradigm can be used to perform experiments with human annotations on-the fly. |
A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check (D18-1)
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| Challenge: | Chinese spelling check (CSC) is a challenging but meaningful task that serves as a preprocessing in many natural language processing(NLP) applications. |
| Approach: | They propose to construct Chinese spelling check corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to OCR- and ASR-based methods. Experimental results demonstrate the effectiveness of the approach. |
| Outcome: | The proposed method is based on visual or phonologically similar spelling errors, and is validated with respect to three standard test sets. |
Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph (2021.findings-acl)
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| Challenge: | Existing methods for stance detection are not applicable to zero-shot and few-shot scenarios. |
| Approach: | They propose a model that integrates commonsense knowledge into a stance detection model. |
| Outcome: | The proposed model outperforms state-of-the-art methods on zero-shot and few-shot stance detection tasks. |
MMCoQA: Conversational Question Answering over Text, Tables, and Images (2022.acl-long)
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| Challenge: | Existing conversational QA systems only use a single knowledge source, e.g., paragraphs or knowledge graph, and assume it contains enough evidence to extract answers to users' questions. |
| Approach: | They propose a task to answer users' questions with multimodal knowledge sources via multi-turn conversations using a multimodal dataset. |
| Outcome: | The proposed task brings a series of research challenges, including but not limited to priority, consistency, and complementarity of multimodal knowledge. |
Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation (2021.eacl-main)
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| Challenge: | Automated Post-Editing (APE) aims to correct errors in the output of a given machine translation system. |
| Approach: | They propose two new methods of synthesizing additional MT outputs by adapting back-translation to the APE task, obtaining robust enlargements of existing synthetic APE training dataset. |
| Outcome: | The proposed methods improve translation quality on the English-German APE task by enlarging the existing training dataset. |
Customizing Grapheme-to-Phoneme System for Non-Trivial Transcription Problems in Bangla Language (N19-1)
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Sudipta Saha Shubha, Nafis Sadeq, Shafayat Ahmed, Md. Nahidul Islam, Muhammad Abdullah Adnan, Md. Yasin Ali Khan, Mohammad Zuberul Islam
| Challenge: | Existing methods for Grapheme to phoneme conversion in Bangla language are mostly rule-based. |
| Approach: | They propose to use a lexicon to train a robust Grapheme to phoneme conversion system in Bangla language. |
| Outcome: | The proposed method outperforms other state-of-the-art approaches for G2P conversion in Bangla language. |
Towards Computational Resource Grammars for Runyankore and Rukiga (2020.lrec-1)
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| Challenge: | In this paper, we present computational resource grammars of Runyankore and Rukiga languages . runyankores and rukiga are under-resourced Bantu languages spoken by 6 million people . |
| Approach: | They present computational resource grammars for Runyankore and Rukiga languages . they use a multilingual grammar formalism and a special- purpose functional programming language . |
| Outcome: | The proposed grammars are the first attempt to create language resources for R&R . they can be used to build computer-aided language learning applications for the languages . |
From Zero to Hero: Cold-Start Anomaly Detection (2024.findings-acl)
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| Challenge: | Existing anomaly detection methods require previous observations to be effective . contaminated observations are often not observed, making them ineffective . |
| Approach: | They propose a method that adapts a zero-shot anomaly detector to contaminated observations . they propose an evaluation suite consisting of evaluation protocols and metrics . |
| Outcome: | The proposed method adapts the zero-shot anomaly detector to contaminated observations. |
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset (D19-1)
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Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Ben Goodrich, Daniel Duckworth, Semih Yavuz, Amit Dubey, Kyu-Young Kim, Andy Cedilnik
| Challenge: | a lack of high quality conversational data is limiting progress in dialog systems . we present a dataset of 13,215 task-based dialogs . |
| Approach: | They propose a task-based dialog dataset which includes 13,215 task-related dialogs . they use a two-person, spoken "Wizard of Oz" approach and a "self-dialog" approach . |
| Outcome: | The taskmaster-1 dataset contains 13,215 task-based dialogs comprising six domains. |
Inquisitive Question Generation for High Level Text Comprehension (2020.emnlp-main)
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| Challenge: | Existing data-driven questions generate questions that fill gaps in knowledge . a dataset of 19K questions is used to generate meaningful questions . |
| Approach: | They propose a dataset of 19K questions that are elicited while a person is reading a document. |
| Outcome: | The proposed model generates reasonable questions, but the task is challenging. |
MultiVerse: Efficient and Expressive Zero-Shot Multi-Task Text-to-Speech (2024.findings-emnlp)
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| Challenge: | Text-to-speech systems that scale up the amount of training data have certain limitations: they require a large amount of data, which increases costs, and overlook prosody similarity. |
| Approach: | They propose a zero-shot multi-task TTS system that can perform TTS or speech style transfer in zero- shot and cross-lingual conditions. |
| Outcome: | The proposed system outperforms other TTS systems trained with the same small amount of data and achieves zero-shot performance comparable to data-driven systems. |
All That Glitters is Not Gold: A Gold Standard of Adjective-Noun Collocations for German (2020.lrec-1)
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| Challenge: | Using the GerCo dataset, we identify adjective-noun collocations in German and compare them with statistical associations measures. |
| Approach: | They present a GerCo dataset of adjective-noun collocations for German, such as alter Freund ‘old friend’ and tiefe Liebe ‘deep love’. |
| Outcome: | The GerCo dataset contains 4,732 positive and negative instances of collocations and covers all 16 semantic classes of adjectives defined in the German wordnet GermaNet. |
MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision (2020.emnlp-main)
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| Challenge: | Existing discourse treebanks are limited in the application of data-driven approaches to discourse parsing. |
| Approach: | They propose a method to automatically generate discourse treebanks using distant supervision from sentiment annotated datasets by heuristic beam-search strategy extended with a stochastic component. |
| Outcome: | The proposed method generates discourse trees incorporating structure and nuclearity for documents of arbitrary length using an efficient beam-search strategy, extended with a stochastic component. |
iSign: A Benchmark for Indian Sign Language Processing (2024.findings-acl)
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Abhinav Joshi, Romit Mohanty, Mounika Kanakanti, Andesha Mangla, Sudeep Choudhary, Monali Barbate, Ashutosh Modi
| Challenge: | Indian Sign Language has limited resources for developing machine learning and data-driven approaches for automated language processing. |
| Approach: | They propose to use a sign language dataset to provide a benchmark for Indian Sign Language processing. |
| Outcome: | The proposed benchmarks will help improve sign language translation models and open up various ways for advancing natural language processing. |
Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning (2020.acl-main)
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| Challenge: | a new method for combining multi-agent communication with traditional data-driven approaches to natural language learning is proposed . we combine the two types of learning with a goal of teaching agents to communicate with humans in natural language. |
| Approach: | They propose a method that combines traditional data-driven approaches to natural language learning with multi-agent self-play environments. |
| Outcome: | The proposed method outperforms other methods in communicating with humans in natural language. |
Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature (2022.emnlp-main)
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| Challenge: | Existing datasets for lay summarisation are limited in size and scope, hindering the development of data-driven approaches. |
| Approach: | They propose to use two new datasets for the lay summarisation of biomedical research articles to characterise their lay summaries. |
| Outcome: | The proposed datasets are compared with existing datasets and show they can be leveraged to support different audiences and applications. |
One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks (2024.findings-emnlp)
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| Challenge: | Morphologically rich languages are notoriously challenging to process for downstream NLP applications. |
| Approach: | They propose a pretrained model for NLP applications involving the morphologically rich language Sanskrit that outperforms previous models by a considerable margin. |
| Outcome: | The proposed model outperforms tokenized models on established Sanskrit word segmentation tasks and matches the current best lexicon-based model. |