Papers with RNN-based
Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq (2020.aacl-demo)
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| Challenge: | End-to-end sequence-to sequence (S2S) modeling has witnessed rapid growth in speech-totext (ST) tasks. |
| Approach: | They introduce fairseq S2T, a fairsq extension for speech-to-text modeling tasks such as end-to end speech recognition and speech-text translation. |
| Outcome: | The proposed extension provides end-to-end workflows from data pre-processing, model training to offline (online) inference. |
Implicit Temporal Reasoning for Evidence-Based Fact-Checking (2023.findings-eacl)
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| Challenge: | Temporal reasoning is implicit since models learn from data how to leverage temporal information. |
| Approach: | They propose to ground claims and associated evidence on shared timelines using publication dates and time expressions extracted from their text. |
| Outcome: | The proposed model outperforms existing models that explicitly model temporal relations between evidence and the document by up to 9% Micro F1 and 15% Macro F1 on the MultiFC dataset. |
RACAI’s System at PharmaCoNER 2019 (D19-57)
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| Challenge: | RACAI researchers develop named entity recognition systems for Romanian language . current system is language-independent and can be improved by using language-dependent resources . |
| Approach: | They propose to train a named entity recognition system for Romanian language . they propose to use a gazetteer-based baseline and a RNN-based NER system . |
| Outcome: | The proposed system is language independent, provided language-dependent resources exist . the proposed system can detect entities with four labels: anatomical parts, disorders, medical procedures and chemical compounds . |
A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment (2021.acl-long)
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| Challenge: | Existing methods for learning word alignment include statistical word aligners (e.g. GIZA++) Existing word alignment models employ a target-to-source attention mechanism which can provide rough word alignments but with a low accuracy. |
| Approach: | They propose a bidirectional Transformer based alignment model for unsupervised learning of the word alignment task. |
| Outcome: | The proposed model outperforms both previous neural word alignment approaches and the popular statistical word aligner GIZA++ on three word alignment tasks. |
ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization (2020.emnlp-main)
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| Challenge: | Cherokee is a highly endangered Native American language spoken by the Cherokee people . there are only 2,000 fluent first language Cherokee speakers remaining in the world . |
| Approach: | They propose a Cherokee-English parallel dataset to facilitate machine translation between Cherokee and English. |
| Outcome: | The proposed dataset compares Cherokee-English and English-Cherokee machine translation systems . the results show that the datasets are low-resource and low-cost compared to other datasets . |
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations (N19-1)
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| Challenge: | Syntax integration has been demonstrated highly effective in neural machine translation (NMT). |
| Approach: | They propose a method to integrate source-side syntax implicitly for neural machine translation . they use hidden representations of a well-trained end-to-end dependency parser to concatenate them with ordinary word embeddings to enhance basic NMT models. |
| Outcome: | The proposed method outperforms existing methods on two translation tasks . it can be easily integrated into the widely-used sequence-to-sequence (Seq2Sequen) framework . |
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions (D18-1)
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| Challenge: | Empirically, PHSIC is learned thousands of times faster than an RNN-based PMI while outperforming PMI in accuracy. |
| Approach: | They propose a new kernel-based co-occurrence measure that can be applied to sparse linguistic expressions with a very short learning time. |
| Outcome: | The proposed measure can be applied to sparse linguistic expressions with a very short learning time, and is called the pointwise HSIC. |
Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation (2020.coling-main)
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| Challenge: | Variational Autoencoders (VAEs) have been widely used in text modelling but posterior collapse is a problem when RNN-based models are employed. |
| Approach: | They propose a timestep-wise regularisation VAE architecture which can effectively avoid posterior collapse when used in text modelling. |
| Outcome: | The proposed model avoids posterior collapse and can be applied to any RNN-based VAE model. |
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder (D19-1)
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| Challenge: | Understanding event and event-centered commonsense reasoning is crucial for natural language processing (NLP). |
| Approach: | They propose a If-Then commonsense reasoning dataset Atomic and an RNN-based Seq2Seq model to facilitate this. |
| Outcome: | The proposed model improves the accuracy and diversity of inferences compared with baseline methods. |