Pointing to Subwords for Generating Function Names in Source Code (2020.coling-main)
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| Challenge: | Existing methods for generating function names from source code face difficulties in generating low-frequency or out-of-vocabulary subwords. |
| Approach: | They propose two strategies for copying low-frequency or out-of-vocabulary subwords in inputs. |
| Outcome: | The proposed method improves on the Java-small and Java-large datasets and improves the existing method on the GitHub platform. |
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More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)
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| Challenge: | Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective . |
| Approach: | They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages . |
| Outcome: | The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders. |
A Systematic Study of Leveraging Subword Information for Learning Word Representations (N19-1)
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| Challenge: | Existing word representation models for morphologically rich languages use subword-level information, but their systematic comparative analysis across typologically diverse languages and tasks is still missing. |
| Approach: | They propose a framework for learning subword-informed word representations that allows for easy experimentation with different segmentation and composition components. |
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Eliciting Instruction-tuned Code Language Models’ Capabilities to Utilize Auxiliary Function for Code Generation (2024.findings-emnlp)
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| Challenge: | Using auxiliary functions to implement functions is important for instruction-tuned models because it reduces the implementation difficulty of a target function compared to implementing them from scratch. |
| Approach: | They propose several ways to provide auxiliary functions to the models by adding them to the query or providing a response prefix to incorporate the ability to utilize auxiliary function with the instruction following capability. |
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Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
Automatic Term Name Generation for Gene Ontology: Task and Dataset (2020.findings-emnlp)
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Yanjian Zhang, Qin Chen, Yiteng Zhang, Zhongyu Wei, Yixu Gao, Jiajie Peng, Zengfeng Huang, Weijian Sun, Xuanjing Huang
| Challenge: | Gene Ontology (GO) terms are used to describe gene function in biology and bio-medicine. |
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| Outcome: | The proposed model outperforms baselines by incorporating the relations between genes, words and terms for term name generation. |
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials (N19-5)
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| Challenge: | NAACL-HLT 2019 tutorials session is organized to give conference attendees a comprehensive introduction to a topic of importance drawn from our rapidly growing and changing research field from expert researchers. |
| Approach: | the tutorials committee at NAACL HLT 2019 received 46 tutorial submissions . 6 of the tutorial submission were selected for presentation at the conference . |
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Subword-Delimited Downsampling for Better Character-Level Translation (2022.findings-emnlp)
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| Challenge: | Subword-level models are expensive in terms of time and computation, but character-level model with downsampling component can be used for machine translation. |
| Approach: | They propose a character-level downsampling method which is informed by subwords to improve model performance. |
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Reusing Weights in Subword-Aware Neural Language Models (N18-1)
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| Challenge: | a statistical language model assigns a probability to a sequence of words . data sparsity is a major problem in building traditional n-gram language models . |
| Approach: | They propose several ways to reuse subword embeddings and other weights in subword-aware neural language models. |
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Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts) (2025.acl-tutorials)
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| Challenge: | ACL 2025 tutorial sessions are a cornerstone event of the conference . 76 tutorial submissions were received this year, many of which were very engaging . |
| Approach: | 76 tutorial submissions were received this year for the tutorial session at ACL 2025 . the tutorials are designed to equip you with the latest insights, tools, and methodologies . |
| Outcome: | the tutorial sessions at ACL 2025 will be held in london on november 8 . the conference received 76 tutorial submissions this year . |
Advances in Pre-Training Distributed Word Representations (L18-1)
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| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
| Approach: | They propose to combine known tricks and a set of publicly available pre-trained word vector representations to train high-quality representations. |
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