9,554 research outputs found
2kenize: Tying Subword Sequences for Chinese Script Conversion
Simplified Chinese to Traditional Chinese character conversion is a common
preprocessing step in Chinese NLP. Despite this, current approaches have poor
performance because they do not take into account that a simplified Chinese
character can correspond to multiple traditional characters. Here, we propose a
model that can disambiguate between mappings and convert between the two
scripts. The model is based on subword segmentation, two language models, as
well as a method for mapping between subword sequences. We further construct
benchmark datasets for topic classification and script conversion. Our proposed
method outperforms previous Chinese Character conversion approaches by 6 points
in accuracy. These results are further confirmed in a downstream application,
where 2kenize is used to convert pretraining dataset for topic classification.
An error analysis reveals that our method's particular strengths are in dealing
with code-mixing and named entities.Comment: Accepted to ACL 202
Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese Hokkien
In natural language processing (NLP), code-mixing (CM) is a challenging task,
especially when the mixed languages include dialects. In Southeast Asian
countries such as Singapore, Indonesia, and Malaysia, Hokkien-Mandarin is the
most widespread code-mixed language pair among Chinese immigrants, and it is
also common in Taiwan. However, dialects such as Hokkien often have a scarcity
of resources and the lack of an official writing system, limiting the
development of dialect CM research. In this paper, we propose a method to
construct a Hokkien-Mandarin CM dataset to mitigate the limitation, overcome
the morphological issue under the Sino-Tibetan language family, and offer an
efficient Hokkien word segmentation method through a linguistics-based toolkit.
Furthermore, we use our proposed dataset and employ transfer learning to train
the XLM (cross-lingual language model) for translation tasks. To fit the
code-mixing scenario, we adapt XLM slightly. We found that by using linguistic
knowledge, rules, and language tags, the model produces good results on CM data
translation while maintaining monolingual translation quality.Comment: The paper was accepted by EMNLP 2022 finding
Natural Language Processing with Small Feed-Forward Networks
We show that small and shallow feed-forward neural networks can achieve near
state-of-the-art results on a range of unstructured and structured language
processing tasks while being considerably cheaper in memory and computational
requirements than deep recurrent models. Motivated by resource-constrained
environments like mobile phones, we showcase simple techniques for obtaining
such small neural network models, and investigate different tradeoffs when
deciding how to allocate a small memory budget.Comment: EMNLP 2017 short pape
Corpus Augmentation by Sentence Segmentation for Low-Resource Neural Machine Translation
Neural Machine Translation (NMT) has been proven to achieve impressive
results. The NMT system translation results depend strongly on the size and
quality of parallel corpora. Nevertheless, for many language pairs, no
rich-resource parallel corpora exist. As described in this paper, we propose a
corpus augmentation method by segmenting long sentences in a corpus using
back-translation and generating pseudo-parallel sentence pairs. The experiment
results of the Japanese-Chinese and Chinese-Japanese translation with
Japanese-Chinese scientific paper excerpt corpus (ASPEC-JC) show that the
method improves translation performance.Comment: 4 pages. The version before Applied. Science
Language Without Words: A Pointillist Model for Natural Language Processing
This paper explores two separate questions: Can we perform natural language
processing tasks without a lexicon?; and, Should we? Existing natural language
processing techniques are either based on words as units or use units such as
grams only for basic classification tasks. How close can a machine come to
reasoning about the meanings of words and phrases in a corpus without using any
lexicon, based only on grams?
Our own motivation for posing this question is based on our efforts to find
popular trends in words and phrases from online Chinese social media. This form
of written Chinese uses so many neologisms, creative character placements, and
combinations of writing systems that it has been dubbed the "Martian Language."
Readers must often use visual queues, audible queues from reading out loud, and
their knowledge and understanding of current events to understand a post. For
analysis of popular trends, the specific problem is that it is difficult to
build a lexicon when the invention of new ways to refer to a word or concept is
easy and common. For natural language processing in general, we argue in this
paper that new uses of language in social media will challenge machines'
abilities to operate with words as the basic unit of understanding, not only in
Chinese but potentially in other languages.Comment: 5 pages, 2 figure
- …