Papers with out-of-vocabulary
A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code (2021.naacl-main)
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| Challenge: | Existing methods to handle out-of-vocabulary identifiers are not suitable for source code processing. |
| Approach: | They propose a method to handle out-of-vocabulary identifiers by identifies anonymization . they show that the method significantly improves the performance of the Transformer . |
| Outcome: | The proposed method significantly improves the performance of the Transformer in two code processing tasks. |
GE2PE: Persian End-to-End Grapheme-to-Phoneme Conversion (2024.findings-emnlp)
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| Challenge: | Existing text-to-speech systems struggle to produce natural speech from grapheme sequences . Grapheme-to phoneme conversion (G2P) systems face limitations when dealing with Persian texts due to the complexity of Persian transcription. |
| Approach: | They propose to use phonetic information to enhance the input sequence for Persian translations. |
| Outcome: | The proposed model surpasses state-of-the-art models by 1.86% in word error rate and 3.42% in homograph disambiguation accuracy. |
Subword-based Compact Reconstruction of Word Embeddings (N19-1)
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| Challenge: | Existing word-based word embeddings are based on subword information and memory-shared embeddables. |
| Approach: | They propose a method for reconstructing pre-trained word embeddings using subword information using memory-shared embedds and a variant of the key-value-query self-attention mechanism. |
| Outcome: | The proposed method can imitate well-trained word embeddings in a small fixed space while preventing quality degradation across several linguistic benchmark datasets. |
Evaluating Tokenizers Impact on OOVs Representation with Transformers Models (2022.lrec-1)
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| Challenge: | Pre-trained Transformer models have proven their effectiveness in adapting to multiple NLP tasks and domains. |
| Approach: | They evaluated three categories of out-of-vocabulary words using three French domain-specific datasets on the legal, medical, and energetical domains to robustly analyze these categories. |
| Outcome: | The proposed models can create new representations for out-of-vocabulary words by adding external morpho-syntactic context rather than improving the semantic understanding of the words directly. |
Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization (P19-1)
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| Challenge: | Abstractive text summarization is a demanding, time expensive and generally laborious task. |
| Approach: | They propose a framework for enhancing abstractive text summarization using deep learning techniques and semantic data transformations. |
| Outcome: | The proposed method is evaluated on two popular datasets with encouraging results. |
OOVs in the Spotlight: How to Inflect Them? (2024.lrec-main)
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| Challenge: | Inflection is a process of word formation in which a base word form (lemma) is modified to express grammatical categories. |
| Approach: | They develop a retrograde model and two sequence-to-sequence models based on LSTM and Transformer. |
| Outcome: | The proposed systems outperform the existing systems on 9 out of 16 languages in the OOV evaluation. |