Out of Vocabulary

material ID: outofvocabulary

Related Papers

Generation of Adaptive Vocabulary Lexicon for Japanese LVCSR

Authors: nan

Publication: TIPSTER (2000)

DOI: nan

One of the thorniest problems of large vocabulary continuous speech recognition systems is the large number of out-of-vocabulary (OOV) words. This is especially the case for the languages like Japanese, …...

18177

A Method for Open-Vocabulary Speech-Driven Text Retrieval

Authors: nan

Publication: TIPSTER (2002)

DOI: nan

While recent retrieval techniques do not limit the number of index terms, out-of- vocabulary (OOV) words are crucial in speech recognition. Aiming at retrieving information with spoken queries, we fill …...

43195

Analysis of Morph-Based Speech Recognition and the Modeling of Out-of-Vocabulary Words Across Languages

Authors: nan

Publication: TIPSTER (2007)

DOI: nan

We analyze subword-based language models (LMs) in large-vocabulary continuous speech recognition across four “morphologically rich” languages: Finnish, Estonian, Turkish, and Egyptian Colloquial Arabic. By estimating n-gram LMs over sequences of …...

24123

The LIG Machine Translation System for WMT 2010

Authors: nan

Publication: TIPSTER (2010)

DOI: nan

This paper describes the system submit- ted by the Laboratory of Informatics of Grenoble (LIG) for the fifth Workshop on Statistical Machine Translation. We participated to the news shared transla- …...

28316

The CMU-ARK German-English Translation System

Authors: nan

Publication: TIPSTER (2011)

DOI: nan

This paper describes the German-English translation system developed by the ARK re- search group at Carnegie Mellon University for the Sixth Workshop on Machine Trans- lation (WMT11). We present the …...

28236

The Karlsruhe Institute of Technology Translation Systems for the WMT 2012

Authors: nan

Publication: TIPSTER (2012)

DOI: nan

This paper describes the phrase-based SMT systems developed for our participation in the WMT12 Shared Translation Task. Translations for English↔German and English↔French were generated using a phrase-based translation system which …...

28122

Sentence Rephrasing for Parsing Sentences with OOV Words

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

AbstractThis paper addresses the problems of out-of-vocabulary (OOV) words, named entities in particular, in dependency parsing. The OOV words, whose word forms are unknown to the learning-based parser, in a …...

16234

Improving Statistical Machine Translation with a Multilingual Paraphrase Database

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

The multilingual Paraphrase Database (PPDB) is a freely available automatically created resource of paraphrases in mul- tiple languages. In statistical machine translation, paraphrases can be used to provide translation for …...

34524

Using BabelNet to Improve OOV Coverage in SMT

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

AbstractOut-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT). This paper studies different strategies of using BabelNet to alleviate the negative impact brought about by …...

16901

Memory-augmented Neural Machine Translation

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractNeural machine translation (NMT) has achieved notable success in recent times, however it is also widely recognized that this approach has limitations with handling infrequent words and word pairs. This …...

32937

Mimicking Word Embeddings using Subword RNNs

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractWord embeddings improve generalization over lexical features by placing each word in a lower-dimensional space, using distributional information obtained from unlabeled data. However, the effectiveness of word embeddings for downstream …...

33107

Improving Pre-Trained Multilingual Model with Vocabulary Expansion

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractRecently, pre-trained language models have achieved remarkable success in a broad range of natural language processing tasks. However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language …...

41719

Handling Rare Entities for Neural Sequence Labeling

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractOne great challenge in neural sequence labeling is the data sparsity problem for rare entity words and phrases. Most of test set entities appear only few times and are even …...

37403

NLPRL System for Very Low Resource Supervised Machine Translation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractThis paper describes the results of the system that we used for the WMT20 very low resource (VLR) supervised MT shared task. For our experiments, we use a byte-level version …...

27502

Representation Learning for Unseen Words by Bridging Subwords to Semantic Networks

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractPre-trained word embeddings are widely used in various fields. However, the coverage of pre-trained word embeddings only includes words that appeared in corpora where pre-trained embeddings are learned. It means …...

14339

Handling Out-Of-Vocabulary Problem in Hangeul Word Embeddings

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractWord embedding is considered an essential factor in improving the performance of various Natural Language Processing (NLP) models. However, it is hardly applicable in real-world datasets as word embedding is …...

42539

Evaluating Tokenizers Impact on OOVs Representation with Transformers Models

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractTransformer models have achieved significant improvements in multiple downstream tasks in recent years. One of the main contributions of Transformers is their ability to create new representations for out-of-vocabulary (OOV) …...

15025

How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractNeural Machine Translation (NMT) is an open vocabulary problem. As a result, dealing with the words not occurring during training (a.k.a. out-of-vocabulary (OOV) words) have long been a fundamental challenge …...

29048

Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractState-of-the-art NLP systems represent inputs with word embeddings, but these are brittle when faced with Out-of-Vocabulary (OOV) words. To address this issue, we follow the principle of mimick-like models to …...

39133