Papers with pretrained word embeddings
VSP at PharmaCoNER 2019: Recognition of Pharmacological Substances, Compounds and Proteins with Recurrent Neural Networks in Spanish Clinical Cases (D19-57)
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| Challenge: | The Named Entity Recognition of drugs, medications and chemical entities in Spanish is a new task in the field of NLP . |
| Approach: | They propose to use SNOMED CT term search engine to classify the entities in Spanish and a neural model for the Named Entity Recognition. |
| Outcome: | The proposed system achieves 76.29% and 60.34% performance in the Named Entity Recognition and Concept indexing tasks. |
Transfer Learning in Natural Language Processing (N19-5)
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| Challenge: | supervised machine learning is based on learning in isolation, a single predictive model for a task using a dataset. |
| Approach: | They present an overview of modern transfer learning methods in natural language processing . they review examples and case studies on how models can be integrated and adapted . |
| Outcome: | The proposed methods improve upon the state-of-the-art on a wide range of NLP tasks. |
Multimodal Machine Translation with Embedding Prediction (N19-3)
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| Challenge: | Pretrained word embeddings improve multimodal machine translation of low-resource domains due to a shortage of training data. |
| Approach: | They propose to combine pretrained word embeddings with search-based approaches to improve NMT of low-resource domains to better translate rare words. |
| Outcome: | The proposed approach improves translation performance by 1.24 METEOR and 2.49 BLEU and achieves 7.67 F-score. |
Comparing Pretrained Multilingual Word Embeddings on an Ontology Alignment Task (L18-1)
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| Challenge: | Existing word embeddings capture a string's semantics and can be trained for multiple languages. |
| Approach: | They propose to compare three different multilingual pretrained word embedding repositories with a string-matching baseline and use it to compute semantic similarities of strings in different languages. |
| Outcome: | The proposed method produces correct alignments on a non-standard dataset on all four languages. |
Extracting Possessions from Social Media: Images Complement Language (D19-1)
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| Challenge: | Existing studies show that authors of tweets possess objects they tweet about. |
| Approach: | They propose a dataset and experiments to determine whether tweet authors possess objects they tweet about. |
| Outcome: | The proposed strategy incorporates visual information into any neural network beyond weights from pretrained networks. |
Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation (N19-1)
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| Challenge: | asynchronous domains lack large labeled datasets to train an effective speech act recognition model. |
| Approach: | They propose methods to leverage abundant unlabeled conversational data and available labeled data from synchronous domains to train an effective SAR model. |
| Outcome: | The proposed method outperforms existing methods when trained on in-domain data only. |
Incorporating Emoji Descriptions Improves Tweet Classification (N19-1)
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| Challenge: | Tweets are short messages that often include specialized language such as hashtags and emojis. |
| Approach: | They propose a simple strategy to replace emojis with their natural language description and use pretrained word embeddings to process tweets. |
| Outcome: | The proposed method is more effective than pretrained emoji embeddings for tweet classification. |
Early Discovery of Disappearing Entities in Microblogs (2023.acl-long)
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| Challenge: | a study on detecting disappearing entities from noisy microblogs has been published on the real world . a major challenge is detecting uncertain contexts of disappearing entity from noisy posts . |
| Approach: | They propose to use Twitter to detect disappearing entities from noisy microblogs . they build large-scale Twitter datasets of disappearing entity and refine word embeddings based on these data . |
| Outcome: | The proposed method outperforms baseline methods on noisy microblog streams and more than 70% of disappearing entities in Wikipedia are discovered earlier than the update on Wikipedia. |
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? (2021.eacl-main)
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| Challenge: | Neural models have established state-of-the-art performance on several NLP benchmarks, but little is understood about the mechanisms by which they operate. |
| Approach: | They examine the probing paradigm through a set of controlled synthetic tasks and show that pretrained word embeddings play a considerable role in encoding these properties rather than the training task itself. |
| Outcome: | The proposed model can encode linguistic properties above chance-level even when distributed in the data as random noise, reversing the interpretation of absolute claims on probing tasks. |
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces (2021.naacl-main)
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| Challenge: | Recent work has used pre-trained word embeddings to address data sparsity in short-text or small document collections. |
| Approach: | They propose a neural topic modeling framework using multi-view embedding spaces to improve topic quality and deal with polysemy. |
| Outcome: | The proposed framework improves topic quality and deal with polysemy. |
Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings (D19-1)
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| Challenge: | Pretrained word embeddings outperforms classifiers with randomly initialized word embeds, a new method is proposed for semi-supervised text classification. |
| Approach: | They propose a method that uses pretrained word embeddings to predict text classification . they use unlabeled data to build a classifier, and use early-stopping to improve performance . |
| Outcome: | The proposed method outperforms self-training and co-training frameworks on unlabeled data. |
An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction (2021.emnlp-main)
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| Challenge: | Current methods for extracting opinion words for an aspect in text leverage position embeddings to capture relative position of word to the target. |
| Approach: | They propose to use pretrained word embeddings to extract opinion words for a given aspect in text. |
| Outcome: | The proposed methods outperform current methods on a task based on pre-trained word embeddings and position embedders. |
Revisiting Tri-training of Dependency Parsers (2021.emnlp-main)
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| Challenge: | Pre-trained word embeddings and self-training have been used in dependency parsing tasks for years. |
| Approach: | They compare tri-training and pretrained word embeddings in dependency parsing . they use language-specific FastText and ELMo embedds and multilingual BERT embedders . |
| Outcome: | The proposed methods are tri-training and pretrained word embeddings. |