Papers with supervised baseline
Label efficient semi-supervised conversational intent classification (2023.acl-industry)
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| Challenge: | A conversational chatbot can answer pre-purchase questions and post-purchase queries to provide a seamless shopping experience. |
| Approach: | They propose a semi-supervised learning approach for label-efficient intent classification using a small labeled corpus and large unlabeled query data to train a transformer model. |
| Outcome: | The proposed approach significantly improves over the baseline, even with a limited labeled set. |
Non-Adversarial Unsupervised Word Translation (D18-1)
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| Challenge: | Unsupervised word translation from non-parallel inter-lingual corpora has attracted much research interest. |
| Approach: | They propose a method that aligns two words in two languages and iteratively refines the alignment. |
| Outcome: | The proposed method achieves better performance than state-of-the-art deep adversarial approaches on word translation of European and Non-European languages. |
Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classification (2026.acl-srw)
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| Challenge: | idiomatic light verb constructions (LVCs) are challenging for multiword expression processing . they share the same surface form as fully literal verb–object combinations . |
| Approach: | They frame Turkish LVC detection as a binary classification task . they compare a supervised Turkish encoder baseline to three instruction-tuned LLMs . |
| Outcome: | The proposed method improves Turkish LVC detection on a controlled set with matched negatives and positives. |
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) like GPT-4 are not able to handle multi-modal open-domain question answering in a zero-shot manner. |
| Approach: | MoqaGPT uses divide-and-conquer strategy to extract answers from each modality separately. |
| Outcome: | MoqaGPT improves on MMCoQA dataset by +37.91 points and EM by +34.07 points. |
Towards an argumentative content search engine using weak supervision (C18-1)
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| Challenge: | Existing work focused on detecting claims within a small set of documents . however, pinpointing relevant claims within massive unstructured corpora, received little attention. |
| Approach: | They propose to use a weak signal to develop a query for claim–sentence detection using a large text corpus. |
| Outcome: | The proposed system outperforms previous results in terms of precision and coverage. |
Language Models as Knowledge Bases? (D19-1)
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Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander Miller
| Challenge: | Recent advances in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. |
| Approach: | They present a method for pretraining language models on large textual corpora . they find that they can store relational knowledge and answer queries structured as "fill-in-the-blank" queries. |
| Outcome: | The proposed language models can recall factual knowledge without fine-tuning without fine tuning . the proposed models can answer queries structured as "fill-in-the-blank" cloze statements . |
GM-RKB WikiText Error Correction Task and Baselines (2020.lrec-1)
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| Challenge: | GM-RKB WikiText Error Correction Task for automatic detection and correction of typographical errors in Wikitext annotated pages. |
| Approach: | They propose to use a GM-RKB semantic wiki to automatically detect typographical errors in WikiText annotated pages. |
| Outcome: | The included corpus is based on a snapshot of the GM-RKB domain-specific semantic wiki consisting of a large collection of concepts, personages, and publications . Numerous Wikipedia pages were also included as additional training data in the task’s evaluation process. |
Merely Judging Metaphor is Not Enough: Research on Reasonable Metaphor Detection (2024.findings-emnlp)
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| Challenge: | Current metaphor detection tasks only provide labels without interpreting how to understand them. |
| Approach: | They propose to improve the current metaphor detection task by using mainstream Large Language Models. |
| Outcome: | The proposed model is based on the original sentence, target word, and usage . the model is then evaluated using manual evaluation . |
Multilingual Detection of Personal Employment Status on Twitter (2022.acl-long)
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| Challenge: | Detecting disclosures of individuals’ employment status on social media is a challenging task due to their rarity in a sea of social media content and the variety of linguistic forms used to describe them. |
| Approach: | They propose to use BERT-based classification models to identify five types of disclosures about individuals’ employment status in three languages. |
| Outcome: | The proposed methods achieve significant gains in precision, recall, and diversity of results in real-world settings of extreme class imbalance. |
Improving NMT Models by Retrofitting Quality Estimators into Trainable Energy Loss (2025.coling-main)
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| Challenge: | Reinforcement learning has shown great promise in aligning language models with human preferences in a variety of text generation tasks, including machine translation. |
| Approach: | They propose a method that employs quality estimators as trainable loss networks to backpropagate to the NMT model. |
| Outcome: | The proposed method outperforms strong baselines and proximal policy optimizations on English-to-Mongolian translation. |
Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders (2020.acl-main)
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| Challenge: | Semantic dependency parsing allows words to have multiple dependency heads, resulting in graph-structured representations. |
| Approach: | They propose an approach to semi-supervised learning of semantic dependency parsers based on the CRF autoencoder framework. |
| Outcome: | The proposed model improves over the baseline model and is arc-factored. |
WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction (2023.acl-long)
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| Challenge: | Existing word alignment methods rely on manual data and lack generalization ability. |
| Approach: | They propose to use a weakly-supervised large-scale weakly supervised dataset for word alignment pre-training via span prediction to reduce the need for manual data. |
| Outcome: | The proposed method improves upon the best supervised baseline by 3.3 6.1 points in F1 and 1.5 6.1 point in AER. |
Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models (2022.lrec-1)
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| Challenge: | Existing methods for creating metaembeddings from static word embeddings have been proposed, but they are not tied to a particular downstream task. |
| Approach: | They propose a sentence-level meta-embedding learning method that takes contextualised word embedding models and learns a phrase embeddable that preserves complementary strengths of the input source NLMs. |
| Outcome: | The proposed method outperforms existing methods on semantic textual similarity benchmarks on a supervised baseline and on token-level embeddings. |
LegalCore: A Dataset for Event Coreference Resolution in Legal Documents (2025.findings-acl)
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Kangda Wei, Xi Shi, Jonathan Tong, null Sai Ramana Reddy, Anandhavelu Natarajan, Rajiv Jain, Aparna Garimella, Ruihong Huang
| Challenge: | Existing research on event coreference resolution is limited to news articles . existing datasets for news articles are limited to events and coreferences . |
| Approach: | They present a dataset for the legal domain LegalCore which has been annotated with event and event coreference information. |
| Outcome: | The legal contract documents annotated in this dataset are several times longer than news articles, with an average length of around 25k tokens per document. |