Papers with sequence labelling task
Improving Segmentation for Technical Support Problems (2020.acl-main)
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| Challenge: | Technical support problems are long and complex and cannot be correctly parsed by tools designed for natural language. |
| Approach: | They propose a sequence labelling task and a supervised text segmentation approach to solve this problem. |
| Outcome: | The proposed approach improves on the downstream task of answer retrieval. |
HiNER: A large Hindi Named Entity Recognition Dataset (2022.lrec-1)
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Rudra Murthy, Pallab Bhattacharjee, Rahul Sharnagat, Jyotsana Khatri, Diptesh Kanojia, Pushpak Bhattacharyya
| Challenge: | Named Entity Recognition (NER) is a lowerlevel task that aims to provide class labels like Person, Location, Organisation, Time, and Number to words in free text. |
| Approach: | They propose to use a standard-abiding Hindi NER dataset to analyze the annotations of a class of naming entities in free text. |
| Outcome: | The proposed dataset achieves a weighted F1 score of 88.78 with all the tags and 92.22 when we collapse the tag-set. |
Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques (2024.acl-long)
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| Challenge: | Recent work on sequence labelling has explored different strategies to mitigate the lack of manually annotated data for the large majority of the world languages. |
| Approach: | They propose to use the mask objective to exploit the few-shot capabilities of pre-trained language models to improve their performance. |
| Outcome: | The proposed model-transfer outperforms data-transference and fine-tuning outperformed few-shot methods for Argument Mining task. |
Weakly Supervised Attention Networks for Entity Recognition (D19-1)
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| Challenge: | Existing approaches to entity recognition require large amounts of token-level data, which can be expensive and cumbersome to obtain. |
| Approach: | They propose a weakly supervised model that can be annotated at word level from a corpus containing binary presence/absence labels. |
| Outcome: | The proposed model performs reasonably well on the task of entity recognition despite not having access to token-level ground truth data. |
Detect and Classify – Joint Span Detection and Classification for Health Outcomes (2021.emnlp-main)
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| Challenge: | Existing methods for detecting health outcomes from text ignore global structural correspondences between sentence-level and word-level information present in a given text. |
| Approach: | They propose a method that uses both word-level and sentence-level information to perform outcome span detection and outcome type classification. |
| Outcome: | The proposed method consistently outperforms decoupled methods, reporting competitive results. |