Papers with Herein
Parallel Corpora for the Biomedical Domain (L18-1)
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| Challenge: | Existing corpora of parallel corporata are being used in the biomedical domain . MT is known to support readers' access to textual documents in a language other than their native language . |
| Approach: | They propose to leverage parallel corpora to implement cross-lingual information retrieval or machine translation tools. |
| Outcome: | The proposed corpus is being used in the biomedical task at the conference on machine translation (WMT'16 and WMT'17) it can be leveraged to provide access to health information in languages other than English. |
Zero-Shot-BERT-Adapters: a Zero-Shot Pipeline for Unknown Intent Detection (2023.findings-emnlp)
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| Challenge: | Intent discovery remains a crucial task in natural language processing . identifying novel, unseen intents remains one of the biggest challenges in this field . |
| Approach: | They propose a multi-language approach to intent discovery using Adapters and a Transformer architecture. |
| Outcome: | The proposed pipeline outperforms baselines in two zero-shot settings for intent classification and unseen intent discovery. |
Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems (2020.emnlp-main)
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| Challenge: | Existing models focus on one-unknown linear MWPs. |
| Approach: | They propose a universal expression tree-structured solver that integrates multiple expression trees underlying a MWP into a single expression tree. |
| Outcome: | The proposed method outperforms state-of-the-art models on a MWPs dataset and generates a universal expression tree explicitly by deciding which symbol to generate . |
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model Compression (2022.coling-1)
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| Challenge: | Knowledge distillation (KD) can transfer knowledge from the original model into a compact model to achieve model compression. |
| Approach: | They propose a knowledge distillation method with reptile meta-learning to facilitate the transfer of knowledge from the teacher to the student. |
| Outcome: | Extensive experiments on the GLUE benchmark show the proposed method performs better than previous methods. |
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks (2021.acl-long)
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| Challenge: | Existing solutions for math word problems lack explicit integration of math symbolic constraints, leading to unexplainable and unreasonable predictions. |
| Approach: | They propose a novel mathematical model that explicitly incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning. |
| Outcome: | The proposed solver incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning. |
I0T: Embedding Standardization Method Towards Zero Modality Gap (2025.acl-long)
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| Challenge: | Recent studies on Contrastive Language-Image Pretraining suffer from a *modality gap* . modality gap occurs when image and text embeddings are projected to disparate manifolds . |
| Approach: | They propose a framework that reduces the modality gap by adding two normalization layers to each encoder. |
| Outcome: | The proposed framework reduces the modality gap while preserving the original embedding representations of trained models with their locked parameters. |
Typos Correction Training against Misspellings from Text-to-Text Transformers (2024.lrec-main)
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| Challenge: | Existing dense retrieval systems suffer from typoed queries due to mistyping or phonetic typing errors. |
| Approach: | They propose a method that incorporates the spelling correction objective into the DR model and a prompt-based augmentation technique to enhance the alignment of the typoed query and its original query. |
| Outcome: | The proposed model outperforms existing typos-aware training approaches and sophisticated training advanced retrievers. |