Papers with Natural language processing
OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)
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Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen
| Challenge: | a large number of natural language processing tasks focus on token-level or sentence-level understandings. |
| Approach: | They propose an open-source and extensible toolkit for various extraction tasks . they deploy an online demo with restful APIs to support real-time extraction . |
| Outcome: | The proposed model can be used to extract information from text without training and deployment. |
VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning (2025.acl-demo)
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Yang Tan, Chen Liu, Jingyuan Gao, Wu Banghao, Mingchen Li, Ruilin Wang, Lingrong Zhang, Huiqun Yu, Guisheng Fan, Liang Hong, Bingxin Zhou
| Challenge: | Pre-trained protein language models have been used in protein engineering, but their adoption is limited due to data collection, task benchmarking, and application challenges. |
| Approach: | They propose a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs. |
| Outcome: | The proposed engine integrates biological data retrieval, task benchmarking, and modular fine-tuning of PLMs. |
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models (2022.lrec-1)
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| Challenge: | Existing studies on NLP touch upon moral contexts in text. |
| Approach: | They construct a dataset that can be used for moral judgement tasks on a popular reddit subreddit. |
| Outcome: | The proposed model passes moral judgements on posts from a popular reddit subreddit . it shows that the model can be fine tuned and improves across the datasets . |
An Information-Theoretic Approach to Analyze NLP Classification Tasks (2024.acl-long)
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| Challenge: | Natural language processing (NLP) tasks take either a single or multiple text elements to predict an output variable. |
| Approach: | They propose an information-theoretic framework to analyse the influence of inputs on the output of text classification tasks. |
| Outcome: | The proposed framework is available at: https://github.com/WangLuran/nlp-element-influence. |
Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks (2022.findings-emnlp)
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| Challenge: | Existing online backdoor defense methods for NLP models focus on anomalies at input or output level, causing fragility to adaptive attacks and high computational cost. |
| Approach: | They propose a feature-based online defense method to detect poisoned samples . they use a distance-based anomaly score to distinguish poisones from clean samples based on feature-level regularization . |
| Outcome: | The proposed method outperforms existing methods in sentiment analysis and offense detection tasks. |
The Importance of Modeling Social Factors of Language: Theory and Practice (2021.naacl-main)
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| Challenge: | Current NLP models focus on information content while ignoring language’s social factors. |
| Approach: | They propose that NLP systems focus on information content while ignoring language’s social factors to improve performance. |
| Outcome: | The proposed approach improves the performance of existing systems, open up new applications, and increase fairness and usability for all users. |
NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages (2023.eacl-main)
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Genta Indra Winata, Alham Fikri Aji, Samuel Cahyawijaya, Rahmad Mahendra, Fajri Koto, Ade Romadhony, Kemal Kurniawan, David Moeljadi, Radityo Eko Prasojo, Pascale Fung, Timothy Baldwin, Jey Han Lau, Rico Sennrich, Sebastian Ruder
| Challenge: | In Indonesia, many languages are endangered and some are even extinct due to the unavailability of data resources and benchmarks. |
| Approach: | They propose a high-quality multilingual parallel corpus that covers 10 local languages from Indonesia. |
| Outcome: | The proposed resource includes sentiment and machine translation datasets, and bilingual lexicons. |
AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports (2024.lrec-main)
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Lukas Lange, Marc Müller, Ghazaleh Haratinezhad Torbati, Dragan Milchevski, Patrick Grau, Subhash Chandra Pujari, Annemarie Friedrich
| Challenge: | Abstract: Natural language processing can help with managing large amounts of unstructured information. |
| Approach: | They propose to annotate a CC-BY-SA-licensed dataset of cyber threat reports . they use named entities, temporal expressions, and cybersecurity-specific concepts . |
| Outcome: | The proposed dataset annotates reports with named entities, temporal expressions, and cybersecurity-specific concepts including implicitly mentioned techniques and tactics. |
Searching for fingerspelled content in American Sign Language (2022.acl-long)
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| Challenge: | Sign language processing is gaining interest in the computer vision and natural language processing communities. |
| Approach: | They propose a model that jointly detects fingerspelling and matches it to a text sequence. |
| Outcome: | The proposed model outperforms baseline methods on a large public dataset of ASL fingerspelling in the wild. |
A Survey on Natural Language Processing for Programming (2024.lrec-main)
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| Challenge: | Natural language processing for programming is a field of NLP and software engineering . it is used to assist programming, and is increasingly prevalent for its effectiveness in improving productivity. |
| Approach: | They propose to use NLP techniques to assist programming by obtaining a structure-based representation and a functionality-oriented algorithm. |
| Outcome: | The proposed approach could relieve developers from laborious work while improving efficiency for non-professional users. |
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings (2021.findings-emnlp)
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| Challenge: | Existing methods to reduce word embedding parameters ignore semantic information . existing methods do not consider semantic information, allowing for performance degradation . |
| Approach: | They propose a method that leverages semantic similarity with weight sharing to reduce dimensionality of word embeddings. |
| Outcome: | The proposed method reduces word embedding parameters by more than 11x on a standard English-German dataset. |
Model Extraction and Adversarial Transferability, Your BERT is Vulnerable! (2021.naacl-main)
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| Challenge: | Pretrained language models are used for natural language processing (NLP) but when they are deployed as a service, they can suffer from different attacks . |
| Approach: | They propose two defence strategies to protect the target model from adversarial attacks . they show that model extraction and adversarially transferable attacks can be effective . |
| Outcome: | The extracted model can lead to highly transferable adversarial attacks against the target model. |
Generalizing Natural Language Analysis through Span-relation Representations (2020.acl-main)
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| Challenge: | a large number of natural language processing tasks are generated with specially designed architectures. |
| Approach: | They propose to represent a wide variety of tasks in a single unified format . they perform extensive experiments to demonstrate benefits of multi-task learning . |
| Outcome: | The proposed model performs comparable to state-of-the-art models on 10 tasks . it also shows that it can analyze differences and similarities in how the model handles different tasks compared to other models . |
EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering (2021.acl-long)
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| Challenge: | Existing studies have shown that data diversity affects the performance of LMs if we train a single LM over the entire dataset. |
| Approach: | They propose an autoencoding topic model with a mixture prior to perform clustering for the data. |
| Outcome: | The proposed model can learn knowledge from different samples while extracting cluster-specific features. |
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities and Responsible Deployment (2026.eacl-long)
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Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Fatima Zahra Moudakir, Deniz Nazar, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Keenan Samway, Dominik Stammbach, Anna Steinberg Schulten, David Tomás, Steven R Wilson, Bowen Yi, Jessica H Zhu, Arkaitz Zubiaga, Anders Søgaard, Alexander Fraser, Zhijing Jin, Rada Mihalcea, Joel R. Tetreault, Daryna Dementieva
| Challenge: | This paper surveys work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Approach: | This paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Outcome: | The paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand (2022.naacl-main)
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Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Lavinia Dunagan, Jacob Morrison, Alexander Fabbri, Yejin Choi, Noah A. Smith
| Challenge: | Recent advances on models and metrics should benefit and inform each other, authors argue . bidimensional leaderboards allow for fast, accurate evaluation of language generation models . |
| Approach: | They propose a bidimensional leaderboard that tracks progress in language generation models and metrics for their evaluation. |
| Outcome: | The proposed leaderboards track progress in language generation models and metrics for their evaluation. |
Speaker Information Can Guide Models to Better Inductive Biases: A Case Study On Predicting Code-Switching (2022.acl-long)
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| Challenge: | Prior approaches for predicting code-switching only consider shallow linguistic context. |
| Approach: | They hypothesize that enriching models with speaker information can guide them to pick up on relevant inductive biases. |
| Outcome: | The proposed model improves on a speaker-driven task in English–Spanish bilingual dialogues by adding sociolinguistically-grounded speaker features as prepended prompts. |
WER we are and WER we think we are (2020.findings-emnlp)
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Piotr Szymański, Piotr Żelasko, Mikolaj Morzy, Adrian Szymczak, Marzena Żyła-Hoppe, Joanna Banaszczak, Lukasz Augustyniak, Jan Mizgajski, Yishay Carmiel
| Challenge: | Recent reports of very low word error rates (WERs) achieved by modern automatic speech recognition systems are skepticism towards the accuracy of modern systems. |
| Approach: | They propose to use a dataset to test automatic speech recognition systems . they propose guidelines for creating real-life datasets with high quality annotations . |
| Outcome: | The proposed system achieves 81% of accuracy on human-chatbot interactions compared to the best reported results on human conversations and public benchmarks. |
On the Ethical Limits of Natural Language Processing on Legal Text (2021.findings-acl)
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| Challenge: | Legal NLP holds the promise of improving access to justice and offers tools for empirical analysis of law on a large scale. |
| Approach: | They propose ways to think systematically about ethical limits of NLP . they place emphasis on three crucial normative parameters that have been underestimated . |
| Outcome: | The proposed methods are based on a real-life scenario that has prompted debate in the legal NLP community. |
Meta-Learning for Effective Multi-task and Multilingual Modelling (2021.eacl-main)
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| Challenge: | Existing studies on multitask and multilingual learning have shown that learning cross-lingual embeddings can benefit multiple tasks and languages. |
| Approach: | They propose a meta-learning approach to learn interactions between tasks and languages . they also investigate the role of different sampling strategies used during meta-learned model . |
| Outcome: | The proposed model improves on five different tasks and six different languages from the XTREME multilingual benchmark dataset. |
Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research (2021.naacl-main)
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| Challenge: | Natural language processing research is often assumed to emerge naturally . many innovations go unapplied and important questions remain unstudied . |
| Approach: | They propose a new paradigm to structure and facilitate the processes by which basic and applied NLP research inform one another. |
| Outcome: | The proposed framework provides a roadmap for developing Translational NLP as a dedicated research area. |
Systematic Inequalities in Language Technology Performance across the World’s Languages (2022.acl-long)
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| Challenge: | Recent studies have revealed that NLP is limited to a subset of the world’s 6,500 languages. |
| Approach: | They propose a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
| Outcome: | The proposed framework estimates the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
HOTTER: Hierarchical Optimal Topic Transport with Explanatory Context Representations (2021.findings-emnlp)
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Sabine Wehnert, Christian Scheel, Simona Szakács-Behling, Maret Nieländer, Patrick Mielke, Ernesto William De Luca
| Challenge: | Natural language processing (NLP) is often the backbone of today’s systems for user interactions, information retrieval and others. |
| Approach: | They propose an extension to a specific emerging hybrid document distance metric which combines topic models and word embeddings. |
| Outcome: | The proposed method is competitive on public datasets and the language model BERT is used for a document categorization task. |
It Is Not About What You Say, It Is About How You Say It: A Surprisingly Simple Approach for Improving Reading Comprehension (2024.findings-acl)
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| Challenge: | Experimenting with 9 large language models across 3 datasets, emphasizing the context yields superior results compared to question emphasis. |
| Approach: | They ask: How does the order of inputs affect model performance? |
| Outcome: | Experiments with 9 large language models show that emphasizing the question and context improves model performance. |
Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies (2021.acl-long)
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| Challenge: | Existing tools to interpret privacy policies have been used to understand them but there is a lack of large privacy policy corpora to simplify the process. |
| Approach: | They propose to use a corpus of 1,005,380 English language privacy policies collected from the web to create semi-supervised and unsupervised models to interpret and simplify privacy policies. |
| Outcome: | The proposed model outperforms all other publicly available privacy policy corpora and is ten times larger than the next largest public collection of privacy policies combined. |
DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation (2022.acl-long)
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| Challenge: | Existing domain adaptation (DA) algorithms are not able to handle out-of-distribution examples due to the costly and labor-intensive data labeling process. |
| Approach: | They propose a controllable generation approach to deal with domain adaptation challenge by generating a domain-counterfactual textual example from an input text. |
| Outcome: | The proposed approach outperforms baselines and improves accuracy of state-of-the-art unsupervised DA algorithm. |
Generating Scientific Definitions with Controllable Complexity (2022.acl-long)
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| Challenge: | Unfamiliar terminology and complex language can make understanding science difficult for readers. |
| Approach: | They propose a task and dataset for defining scientific terms and controlling the complexity of generated definitions by a sequence-to-sequence approach. |
| Outcome: | The proposed system is based on a sequence-to-sequence approach and human evaluations show it offers superior fluency while controlling complexity. |
An Inclusive Notion of Text (2023.acl-long)
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| Challenge: | despite its central role, the notion of text in natural language processing is vague, authors argue . a conceptual framework for capturing text differences is lacking, authors say . authors propose a two-tier taxonomy of linguistic and non-linguistic elements available in textual sources . |
| Approach: | They propose a taxonomy of linguistic and non-linguistic elements available in textual sources and can be used in NLP modeling. |
| Outcome: | The proposed taxonomy examines the production and transformation of textual data . it outlines key desiderata and challenges of the emerging inclusive approach to text in NLP . |
VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls (2020.emnlp-main)
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| Challenge: | Existing approaches to stock volatility forecasting ignore correlations between stocks. |
| Approach: | They propose to combine vocal cues with verbal and financial cue data to create a multimodal stock volatility prediction model that accounts for stock interdependence via graph convolutions. |
| Outcome: | The proposed model outperforms existing methods showing that it can predict volatility using multimodal learning. |
FRASIMED: A Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection (2024.lrec-main)
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Jamil Zaghir, Mina Bjelogrlic, Jean-Philippe Goldman, Soukaïna Aananou, Christophe Gaudet-Blavignac, Christian Lovis
| Challenge: | Existing methods for generating annotations for large datasets are time-consuming and resource-intensive. |
| Approach: | They propose a method for generating translated versions of annotated datasets through crosslingual annotation projection. |
| Outcome: | The proposed method shows that it is efficient and high-quality in the resulting dataset. |
Experience Grounds Language (2020.emnlp-main)
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Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph Turian
| Challenge: | aaron carroll: language understanding research is held back by a failure to relate language to the physical world it describes and to social interactions it facilitates. carroll says successful linguistic communication relies on a shared experience of the world. |
| Approach: | They propose to use a broader physical and social context to address communication problems . they argue that the current success of representation learning approaches is limited . |
| Outcome: | a new study suggests that the current success of representation learning requires a parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication. |
Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions (2024.emnlp-main)
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| Challenge: | Building socially-intelligent AI agents involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents. |
| Approach: | They propose a set of technical challenges and open questions for researchers to advance Social-AI. |
| Outcome: | The proposed frameworks are based on the social intelligence competencies that evolved over thousands of years in Homo sapiens and are expected to be the foundations for the development of social-intelligent AI agents. |