Papers with downstream natural language processing tasks
Improving Graph-Based Text Representations with Character and Word Level N-grams (2022.aacl-short)
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| Challenge: | Graph-based text representation is important in downstream natural language processing tasks. |
| Approach: | They propose a heterogeneous word-character text graph that combines word and character n-gram nodes together with document nodes. |
| Outcome: | The proposed graph outperforms baselines and state-of-the-art models in text classification and automatic summarization. |
Field Embedding: A Unified Grain-Based Framework for Word Representation (2021.naacl-main)
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| Challenge: | Current methods focus on learning word embeddings while linguistic information is discarded after the learning. |
| Approach: | They propose a framework field embedding to jointly learn word and grain embedds by incorporating morphological, phonetic, and syntactical linguistic fields. |
| Outcome: | The proposed framework integrates morphological, phonetic, and syntactical linguistic fields to learn word embeddings and grain embedds. |
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus (2021.acl-long)
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| Challenge: | QASR is the largest transcribed Arabic speech corpus in the broadcast domain. |
| Approach: | They introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. |
| Outcome: | The proposed dataset contains 2,000 hours of speech sampled at 16kHz crawled from Aljazeera news channel. |
TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths (2021.emnlp-main)
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| Challenge: | Existing taxonomies are unable to maintain coverage due to the rising of new concepts . TEMP uses pre-trained contextual encoders to predict the position of new ideas . |
| Approach: | They propose a self-supervised taxonomy expansion method that ranks taxonomies by ranking them . they use pre-trained contextual encoders to train the model with dynamic margin loss . |
| Outcome: | The proposed method outperforms state-of-the-art taxonomy expansion methods by 14.3% and 15.8% on public benchmarks. |
PEER: Pre-training ELECTRA Extended by Ranking (2023.findings-acl)
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| Challenge: | Existing models for pre-training require expensive pre-trainer computation cost . ELECTRA model can perform replaced token detection (RTD) task with reduced pre- training cost compared to current models . |
| Approach: | They propose to extend a discriminator-based replaced token detection task into a ranker-based task . they propose to use a binary classifier to perform a more precise task with negligible additional computation cost. |
| Outcome: | The proposed model outperforms state-of-the-art models with ELECTRA in GLUE tasks given the same cost. |
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations (D19-1)
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| Challenge: | Contextualized word representations are effective in downstream tasks such as question answering, named entity recognition, and sentiment analysis. |
| Approach: | They propose to integrate pre-trained contextualized word representations into a neural network that captures the whole sentence and the word representation in the sentence. |
| Outcome: | The proposed approach outperforms the state-of-the-art approach that makes use of non-contextualized word embeddings on multiple benchmark WSD datasets. |
Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models (2024.acl-long)
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| Challenge: | Modern large language models (LLMs) contain billions of parameters and can perform a variety of downstream tasks. |
| Approach: | They propose an open-source framework for fine-tuning large language models (LLMs) they address key challenges facing LLMs fine- tuned for simultaneous translation . |
| Outcome: | The proposed framework validates classical SimulMT concepts and practices in the context of LLMs and explores adapting LLM fine-tuned for NMT to the task of Simul-LLM. |