Crossing Domains without Labels: Distant Supervision for Term Extraction (2025.emnlp-industry)
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| Challenge: | Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. |
| Approach: | They propose a benchmark spanning seven diverse domains to evaluate ATE performance . they propose psuedo-labels and post-hoc heuristics to ensure generalizability . |
| Outcome: | The proposed model outperforms supervised cross-domain encoder models and few-shot learning baselines on the document- and corpus-levels and its GPT-4o teacher on the benchmark. |
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Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data (2022.lrec-1)
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| Challenge: | Automatic Term Extraction (ATE) is a key component for domain knowledge understanding and can be used for further NLP applications. |
| Approach: | They propose to fine-tune pre-trained BERT models for automatic Term Extraction (ATE) using cross-lingual and cross-domain transfer learning to extract single and multi-word terms. |
| Outcome: | The proposed models can capture cross-domain and cross-lingual terminologically-marked contexts shared by terms, opening a new design-pattern for ATE. |
Transforming Term Extraction: Transformer-Based Approaches to Multilingual Term Extraction Across Domains (2021.findings-acl)
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| Challenge: | Automated Term Extraction (ATE) is a challenging task, with few exceptions. |
| Approach: | They propose to use a transformer-based term extraction model to extract terms from sentences . they also propose to employ a language model for token classification and a sequence model to reduce sentences to terms . |
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Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have improved IE, but their potential for ATE has not been explored. |
| Approach: | They propose a retrieval-based prompting strategy that selects demonstrations according to syntactic rather than semantic similarity in a few-shot setting. |
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A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents (L18-1)
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| Challenge: | Terms are notoriously difficult to identify, both automatically and manually. |
| Approach: | They propose a method to annotate terms manually from a comparable corpus . they show that the gold standard provides a tool for evaluation and a rich source of information . |
| Outcome: | The proposed method provides a tool for evaluation and rich source of information about terms. |
Unsupervised Term Extraction for Highly Technical Domains (2022.emnlp-industry)
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| Challenge: | Term extraction is an important task for knowledge discovery platforms because domain specific terms are the linguistic representation of domainspecific concepts. |
| Approach: | They propose a term extraction subsystem that uses an unsupervised annotator to generate training data to fine-tune transformer models. |
| Outcome: | The proposed system can generalize across domains while reducing latency and inference time while preserving the high performance of the existing system. |
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference (2021.eacl-main)
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| Challenge: | Existing approaches to learning from examples are limited due to the vast number of languages, domains and tasks. |
| Approach: | They propose a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. |
| Outcome: | The proposed approach outperforms supervised training and strong semi-supervised approaches in low-resource settings by a large margin. |
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup (2022.findings-emnlp)
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| Challenge: | Existing approaches to train models to provide natural language explanations (NLEs) require acquisition of task-specific NLEs, which is time- and resource-consuming. |
| Approach: | They propose a few-shot out-of-domain transfer of NLEs from a parent task to a child task . they propose four methods that cover possible fine-tuning combinations of NLESs and labels . |
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Smart “Chef”: Verifying the Effect of Role-based Paraphrasing for Aspect Term Extraction (2023.findings-emnlp)
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| Challenge: | Aspect Term Extraction (ATE) is a task of automatically extracting aspect terms from sentences. |
| Approach: | They propose to automatically rewrite sentences from virtual experts with different roles . they leverage ChatGPT to determine virtual experts in the considered domains . |
| Outcome: | The proposed method can be used to expand the predictions obtained on the original sentences without retraining or fine-tuning the baseline extractors. |
Rethinking the Role of LLMs for Document-level Relation Extraction: a Refiner with Task Distribution and Probability Fusion (2025.naacl-long)
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| Challenge: | Document-level relation extraction (DocRE) provides a broad context for extracting relations for entities. |
| Approach: | They propose a method that utilizes LLMs as a refiner and task distribution and probability fusion to refine LLM-based relation extraction methods. |
| Outcome: | The proposed method outperforms existing LLM-based methods without fine-tuning by 25.2% F1. |
Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging (D18-1)
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| Challenge: | Low-resource languages lack manual annotated data to learn basic models such as part-of-speech (POS) taggers. |
| Approach: | They propose a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision in a uniform framework. |
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