Papers with Github
fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP (2021.acl-demo)
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| Challenge: | Recently, the need for Chinese natural language processing (NLP) has a dramatic increase for many downstream applications. |
| Approach: | They propose to use Chinese word segmentation (CWS), Part-of-Speech (POS) tagging, named entity recognition (NER), and dependency parsing to train a multi-task model based on a pruned BERT. |
| Outcome: | The proposed model performs better than popular segmentation tools on a non-training corpus. |
PDFTriage: Question Answering over Long, Structured Documents (2024.emnlp-industry)
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| Challenge: | Existing approaches to document QA use a pre-retrieval step to retrieve the relevant context from documents, but this is incongruous with the user's mental model of the document. |
| Approach: | They propose an approach called PDFTriage that enables models to retrieve the context based on either structure or content. |
| Outcome: | The proposed approach can retrieve context based on structure or content across several classes of questions where existing retrieval-augmented LLMs fail. |
Filtered Semi-Markov CRF (2023.findings-emnlp)
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| Challenge: | Existing methods for sequence labeling tasks such as Named Entity Recognition (NER) suffer from quadratic complexity over sequence length and poor performance compared to CRF. |
| Approach: | They propose a variant of Semi-Markov CRF that incorporates a filtering step to eliminate irrelevant segments, reducing complexity and search space. |
| Outcome: | The proposed method outperforms both CRF and Semi-CRF on several NER benchmarks while being significantly faster. |
ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems (2024.naacl-long)
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| Challenge: | Evaluating retrieval-augmented generation systems relies on hand annotations for input queries, passages to retrieve, and responses to generate. |
| Approach: | They propose an automated evaluation framework for retrieval-augmented generation (RAG) ARES fine tunes lightweight LLM judges on synthetically generated queries and answers . |
| Outcome: | The proposed framework evaluates RAG systems using only human annotations . it can be used to improve system understanding and create targeted solutions . |
Deep Differential Amplifier for Extractive Summarization (2021.acl-long)
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| Challenge: | Existing approaches to extract summary from document with a disproportionate ratio of selected and unselected sentences are far from human performance. |
| Approach: | They propose a model that rebalances sentence-level extractive summarization by amplifying the semantic difference between each sentence and all other sentences and applying the residual unit as the second item of the differential amplifier to deepen the architecture. |
| Outcome: | The proposed model performs competitively against state-of-the-art methods on two benchmark datasets. |
RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation (2026.eacl-demo)
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| Challenge: | Existing tools for evaluating RAG systems often collapse heterogeneous behaviors into single scores. |
| Approach: | They propose a diagnostic framework for automated, reference-free evaluation of RAG pipelines. |
| Outcome: | The proposed framework decomposes RAG behavior into retrieval quality,answer relevance and completeness, strictclaim-level faithfulness, and judge calibration. |
Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup (2020.acl-srw)
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| Challenge: | Towards this goal, many studies have focused on disaster-related tweet classification. |
| Approach: | They compile a multilingual dataset for multi-label classification of disaster-related tweets . they show that their model generalizes to unseen disasters in the test set . |
| Outcome: | The proposed model generalizes to unseen disasters and improves with Manifold Mixup. |
MSciNLI: A Diverse Benchmark for Scientific Natural Language Inference (2024.naacl-long)
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| Challenge: | a dataset containing 132,320 sentence pairs from five new scientific domains is used for scientific Natural Language Inference (NLI) the availability of multiple domains makes it possible to study domain shift for scientific NLI. |
| Approach: | They propose a dataset with 132,320 sentence pairs from five new scientific domains to introduce diversity in scientific NLI. |
| Outcome: | The proposed dataset contains 132,320 sentence pairs extracted from five new scientific domains. |
Transc&Anno: A Graphical Tool for the Transcription and On-the-Fly Annotation of Handwritten Documents (L18-1)
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| Challenge: | Transc&Anno is a web-based collaboration tool for linguists to facilitate the transcription of text images and their shallow on-the-fly annotation. |
| Approach: | They propose a web-based collaboration tool that allows the transcription of text images and their shallow on-the-fly annotation. |
| Outcome: | The Transc&Anno tool can be used for any type of corpora requiring transcription and shallow on-the-fly annotation resulting in inline XML. |
Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning (2023.emnlp-main)
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| Challenge: | Language model probing is often used to test specific capabilities of models, but results are limited when benchmarks are small and lack statistical power. |
| Approach: | They extend existing NEG-136 and ROLE-88 benchmarks to 750 sentence pairs and create an extended negation dataset using template-based generation. |
| Outcome: | The proposed datasets show that models perform better on the extended datasets than on the smaller benchmarks. |
Weighted self Distillation for Chinese word segmentation (2022.findings-acl)
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| Challenge: | Recent researches show that multi-criteria resources and n-gram features are beneficial to Chinese word segmentation (CWS). |
| Approach: | They propose a framework that uses weighted self distillation to learn Chinese word segmentation using unigram features. |
| Outcome: | The proposed framework achieves state-of-the-art or competitive performance on SIGHAN Bakeoff datasets. |
Co-training for Low Resource Scientific Natural Language Inference (2024.acl-long)
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| Challenge: | Scientific Natural Language Inference (NLI) is a task to predict the semantic relation between sentences extracted from research articles. |
| Approach: | They propose a co-training method that assigns weights based on the training dynamics of the classifiers to the distantly supervised labels. |
| Outcome: | The proposed method achieves an improvement of 1.5% in Macro F1 over the distant supervision baseline and substantial improvements over several other strong SSL baselines. |
MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition (2023.emnlp-main)
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| Challenge: | Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with knowledge bases or gazetteers and unlabeled corpus. |
| Approach: | They propose a noise-robust prototype network named MProto for a DS-NER task . they propose an optimal transport algorithm to mitigate the noise from incomplete labeling . |
| Outcome: | The proposed network achieves state-of-the-art on several DS-NER benchmarks. |
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions (2021.emnlp-main)
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| Challenge: | Keyword or keyphrase extraction is to identify words or phrases presenting the main topics of a document. |
| Approach: | They propose a hybrid attention model to identify keyphrases from a document in an unsupervised manner. |
| Outcome: | The proposed model is effective and robust on long and short documents. |
QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios (2024.emnlp-main)
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| Challenge: | Existing probabilistic reasoning datasets require the model to only rank textual alternatives or use limited set of templates. |
| Approach: | They propose a question-answering dataset that uses probabilistic rules to express degrees of certainty. |
| Outcome: | The proposed model outperforms existing models on all reasoning types . it is available on Github and is expected to be used in clinical documentation . |
Mask Tokens as Prophet: Fine-Grained Cache Eviction for Efficient dLLM Inference (2026.findings-acl)
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| Challenge: | Existing cache eviction strategies for autoregressive language models fail to account for the role of mask tokens and specific characteristics in dLLMs. |
| Approach: | They propose a training-free cache eviction framework tailored to dLLMs that denies a fully masked sequence and allows parallel decoding at the expense of memory and computation. |
| Outcome: | The proposed framework reduces the cost of memory and cache eviction and improves efficiency by reducing allocation in intermediate layers and concentrating resources on prompt-preferring heads. |
Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling (2020.findings-emnlp)
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| Challenge: | Existing methods to verify scientifically false online information are limited by the lack of training data in the scientific domain. |
| Approach: | They propose an in-domain language modeling method for fact extraction and verification systems . they use SCIFACT to extract scientifically false online information . |
| Outcome: | The proposed method improves accuracy 30% on SCIFACT dataset . state-of-the-art model achieves only 46.6% precision, which is hard to be trusted for users. |
GLoHBCD: A Naturalistic German Dataset for Language of Health Behaviour Change on Online Support Forums (2022.lrec-1)
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| Challenge: | Existing motivational interviewing methods lack the deep understanding of user utterances that is essential to the spirit of motivational interviews. |
| Approach: | They propose to use a German dataset of naturalistic language around health behaviour change to examine the motivational state of the user. |
| Outcome: | The proposed dataset of naturalistic language around health behaviour change is based on a weight loss forum in germany and is evaluated using theoretically grounded motivational interviewing categories. |
A Concise Model for Multi-Criteria Chinese Word Segmentation with Transformer Encoder (2020.findings-emnlp)
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| Challenge: | Existing work on multicriteria Chinese word segmentation focuses on combining multiple heterogeneous segmentation criteria into a single task. |
| Approach: | They propose a unified model for multi-criteria Chinese word segmentation which is fully-shared for all criteria. |
| Outcome: | The proposed model outperforms existing models on eight datasets with different criteria. |
Knowledge Graph-Driven Memory Editing with Directional Interventions (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are hampered by inaccuracies and outdated information. |
| Approach: | They propose a framework that constructs knowledge graphs using available information to guide the direction of knowledge editing. |
| Outcome: | The proposed framework allows consistent, aligned, and stable information during large-scale editing scenarios. |
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings (N19-1)
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| Challenge: | Existing approaches to Chinese word segmentation (CWS) are character-based and word-based . character-driven approaches use conditional random field models to label sequences, with complex hand-crafted discrete features. |
| Approach: | They propose a semi-supervised word-based approach to improve cross-domain Chinese word segmentation given a baseline segmenter. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on five datasets covering domains in novels, medicine, and patent. |
Klexikon: A German Dataset for Joint Summarization and Simplification (2022.lrec-1)
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| Challenge: | Traditionally, Text Simplification is a monolingual translation task where individual sentences are "translated" into a simplified version. |
| Approach: | They propose to use a dataset to jointly simplify long source documents by combining sentences from a source and their simplified counterparts. |
| Outcome: | The proposed system can summarize and simplify long source documents using almost 2,900 documents. |
Heterogeneous Graph Neural Networks for Extractive Document Summarization (2020.acl-main)
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| Challenge: | Existing models capture cross-sentence relations with recurrent neural networks, but they are hard to capture sentence-level long-distance dependency. |
| Approach: | They propose a graph-based neural network for extractive summarization which contains semantic nodes apart from sentences. |
| Outcome: | The proposed graph-based neural network is the first to incorporate different types of nodes into it and perform a qualitative analysis. |
An Improved, Strong Baseline for Pre-Trained Large Language Models as Task-Oriented Dialogue Systems (2025.findings-emnlp)
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| Challenge: | Recent studies have shown that Large Language Models perform insufficiently as TOD systems. |
| Approach: | They propose a self-checking mechanism to improve LLM performance as TOD systems. |
| Outcome: | The proposed model outperforms existing models and improves their performance. |
AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment (2025.coling-main)
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Umair Nawaz, Awais Muhammad, Hanan Gani, Muzammal Naseer, Fahad Shahbaz Khan, Salman Khan, Rao Anwer
| Challenge: | Recent studies have addressed this problem by building domain-specialized image-text data. |
| Approach: | They propose a vision-language foundational model dedicated to agriculture and livestock . they propose combining contrastive and self-supervised learning to learn fine-grained features . |
| Outcome: | The proposed model achieves 9.07% gain over standard CLIP training on 20 tasks. |
Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression (2024.findings-emnlp)
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| Challenge: | Existing methods for detecting out-of-distribution data are computationally complex and storage-intensive. |
| Approach: | They propose a soft clustering approach for OOD detection based on non-negative kernel regression . their approach greatly reduces computational and space complexities while retaining competitive performance. |
| Outcome: | The proposed approach outperforms existing methods by up to 4 AUROC points on four benchmarks while retaining competitive performance. |
AnCast++: Document-Level Evaluation of Graph-based Meaning Representations (2025.findings-acl)
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| Challenge: | Abstract Meaning Representation (UMR) is a cross-lingual document-level graph-based representation that extends it to document- level semantic annotations. |
| Approach: | They propose an evaluation metric that unifies evaluation of four distinct sub-structures of UMR. |
| Outcome: | The proposed metric is made available on Github. |
FFAEval: Evaluating Dialogue System via Free-For-All Ranking (2023.findings-emnlp)
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| Challenge: | Existing evaluation metrics for open-domain dialogue systems show poor correlation with human assessment. |
| Approach: | They propose a free-for-all human evaluation framework that shares dialogue history with annotators for multi-turn scoring. |
| Outcome: | The proposed framework achieves a strong correlation with human assessment on English and Chinese dialogue systems. |
GraphKV: Breaking the Static Selection Paradigm with Graph-Based KV Cache Eviction (2025.emnlp-main)
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| Challenge: | Efficient Key-Value (KV) cache management is essential for processing long text sequences in large language models (LLMs). |
| Approach: | They propose a graph-based framework that redefines token selection for KV cache compression. |
| Outcome: | The proposed framework can be used in existing KV cache eviction methods such as SnapKV and PyramidKV in a plug-and-play manner. |
Unveiling the Potential of BERT-family: A New Recipe for Building Scalable, General and Competitive Large Language Models (2025.acl-long)
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| Challenge: | Generative large language models (LLMs) have significantly influenced various aspects of society, reshaping how we access and interact with information and knowledge. |
| Approach: | They propose a pre-training task that helps BERT-family excel in wider applications . they also explore the integration of cutting-edge technologies into their models to further enhance their capabilities. |
| Outcome: | The proposed model exhibits performance levels comparable to current SOTA LLMs across a spectrum of tasks. |
An Empirical Study of Iterative Refinements for Non-autoregressive Translation (2025.acl-long)
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| Challenge: | Iterative non-autoregressive (NAR) models have recently demonstrated impressive performance in varied generation tasks, surpassing the autoregressive Transformer. |
| Approach: | They propose a strategy to conduct efficient refinements without performance declines by using two simple metrics to identify potential problems existing in current refinement processes. |
| Outcome: | The proposed model outperforms the autoregressive Transformer by around one BLEU on average. |