51,326 research outputs found
Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation
The neural language models (NLM) achieve strong generalization capability by
learning the dense representation of words and using them to estimate
probability distribution function. However, learning the representation of rare
words is a challenging problem causing the NLM to produce unreliable
probability estimates. To address this problem, we propose a method to enrich
representations of rare words in pre-trained NLM and consequently improve its
probability estimation performance. The proposed method augments the word
embedding matrices of pre-trained NLM while keeping other parameters unchanged.
Specifically, our method updates the embedding vectors of rare words using
embedding vectors of other semantically and syntactically similar words. To
evaluate the proposed method, we enrich the rare street names in the
pre-trained NLM and use it to rescore 100-best hypotheses output from the
Singapore English speech recognition system. The enriched NLM reduces the word
error rate by 6% relative and improves the recognition accuracy of the rare
words by 16% absolute as compared to the baseline NLM.Comment: 5 pages, 2 figures, accepted to INTERSPEECH 201
Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling
Fixed-vocabulary language models fail to account for one of the most
characteristic statistical facts of natural language: the frequent creation and
reuse of new word types. Although character-level language models offer a
partial solution in that they can create word types not attested in the
training corpus, they do not capture the "bursty" distribution of such words.
In this paper, we augment a hierarchical LSTM language model that generates
sequences of word tokens character by character with a caching mechanism that
learns to reuse previously generated words. To validate our model we construct
a new open-vocabulary language modeling corpus (the Multilingual Wikipedia
Corpus, MWC) from comparable Wikipedia articles in 7 typologically diverse
languages and demonstrate the effectiveness of our model across this range of
languages.Comment: ACL 201
Skip-Thought Vectors
We describe an approach for unsupervised learning of a generic, distributed
sentence encoder. Using the continuity of text from books, we train an
encoder-decoder model that tries to reconstruct the surrounding sentences of an
encoded passage. Sentences that share semantic and syntactic properties are
thus mapped to similar vector representations. We next introduce a simple
vocabulary expansion method to encode words that were not seen as part of
training, allowing us to expand our vocabulary to a million words. After
training our model, we extract and evaluate our vectors with linear models on 8
tasks: semantic relatedness, paraphrase detection, image-sentence ranking,
question-type classification and 4 benchmark sentiment and subjectivity
datasets. The end result is an off-the-shelf encoder that can produce highly
generic sentence representations that are robust and perform well in practice.
We will make our encoder publicly available.Comment: 11 page
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