Papers with GloVe embeddings
Using Word Embeddings to Analyze Teacher Evaluations: An Application to a Filipino Education Non-Profit Organization (2021.findings-acl)
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| Challenge: | Using natural language processing, we examine teacher evaluations to improve educational programs. |
| Approach: | They apply natural language processing techniques to a Filipino education non-profit to analyze teacher evaluations written by Teacher Fellows. |
| Outcome: | The proposed framework can be applied to teacher evaluations from a Filipino education non-profit. |
Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling (2022.coling-1)
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| Challenge: | Existing studies have highlighted a variety of biases in pre-trained language models . however, these studies focus on fine-grained analysis of educational corpora and text that is not English . |
| Approach: | They analyze bias across text and through multiple architectures on a corpus of 9,165 German peer-reviews collected from university students over five years. |
| Outcome: | The proposed dataset shows that pre-trained language models exhibit conceptual, racial, and gender biases. |
Embedding Imputation with Grounded Language Information (P19-1)
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| Challenge: | Existing approaches to embedding imputation use vector space properties or subword information to learn representations for rare or unseen words. |
| Approach: | They propose an online method to construct a knowledge graph from grounded information and an algorithm to map from the resulting graph to the space of the pre-trained embeddings. |
| Outcome: | The proposed method improves on a card-660 task by 11% and 17.8% respectively using GloVe embeddings. |
Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering (2020.acl-main)
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| Challenge: | Evidence retrieval is a critical stage of question answering (QA) . Several multi-hop QA datasets have been proposed recently . |
| Approach: | They propose an unsupervised method that uses only GloVe embeddings to soft-align questions with justification sentences and an iterative process that reformulates queries focusing on terms that are not covered by existing justifications. |
| Outcome: | The proposed method outperforms all previous methods on the evidence selection task on two datasets: MultiRC and QASC. |
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks (D19-1)
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| Challenge: | Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. |
| Approach: | They propose a target-dependent graph attention network for aspect level sentiment classification . it explicitly utilizes the dependency relationship among words to propagate sentiment features . they show that using BERT representations further substantially boosts the performance . |
| Outcome: | The proposed method outperforms baselines with GloVe embeddings and improves with BERT representations. |
Evaluating Word Expansion for Multilingual Sentiment Analysis of Parliamentary Speech (2024.lrec-main)
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| Challenge: | Recent efforts to create and format data sets of parliamentary speech material have facilitated cross-lingual comparisons and highlighted the need for methods that are computationally efficient and language-agnostic. |
| Approach: | They propose a word expansion method for sentiment lexicon generation that leverages word embeddings and vector similarity to expand synonym seed lists with domain-specific terms from the speech corpora. |
| Outcome: | The proposed method is compared with other multilingual lexica and is highly sensitive to processing and scoring techniques. |
ManNER & ManPOS: Pioneering NLP for Endangered Manchu Language (2024.lrec-main)
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| Challenge: | a new study examines the impact of natural language processing (NLP) on the endangered Manchu language. |
| Approach: | They propose to use BiLSTM-CRF, BERT, and mBERT to train transformer-based models on Manchu for NER and POS tagging tasks. |
| Outcome: | The proposed models achieved over 90% F1 score in both NER and POS tasks. |