Reverse dictionary is the task to find the proper target word given the word
description. In this paper, we tried to incorporate BERT into this task.
However, since BERT is based on the byte-pair-encoding (BPE) subword encoding,
it is nontrivial to make BERT generate a word given the description. We propose
a simple but effective method to make BERT generate the target word for this
specific task. Besides, the cross-lingual reverse dictionary is the task to
find the proper target word described in another language. Previous models have
to keep two different word embeddings and learn to align these embeddings.
Nevertheless, by using the Multilingual BERT (mBERT), we can efficiently
conduct the cross-lingual reverse dictionary with one subword embedding, and
the alignment between languages is not necessary. More importantly, mBERT can
achieve remarkable cross-lingual reverse dictionary performance even without
the parallel corpus, which means it can conduct the cross-lingual reverse
dictionary with only corresponding monolingual data. Code is publicly available
at this https URL

本文研究怎样使用 BERT 模型来完成 Reverse Dictionary 的任务，并结合 Multilingual BERT 模型实现多语言反向词典转换，同时提出了一种有效的方法来解决 BERT 模型生成词的问题。该模型在没有平行语料库的情况下，也能够实现出色的跨语言反向词典性能。

BERT 用于单语和跨语言逆词典

BERT for Monolingual and Cross-Lingual Reverse Dictionary

We investigate a lattice LSTM network for Chinese word segmentation (CWS) to
utilize words or subwords. It integrates the character sequence features with
all subsequences information matched from a lexicon. The matched subsequences
serve as information shortcut tunnels which link their start and end characters
directly. Gated units are used to control the contribution of multiple input
links. Through formula derivation and comparison, we show that the lattice LSTM
is an extension of the standard LSTM with the ability to take multiple inputs.
Previous lattice LSTM model takes word embeddings as the lexicon input, we
prove that subword encoding can give the comparable performance and has the
benefit of not relying on any external segmentor. The contribution of lattice
LSTM comes from both lexicon and pretrained embeddings information, we find
that the lexicon information contributes more than the pretrained embeddings
information through controlled experiments. Our experiments show that the
lattice structure with subword encoding gives competitive or better results
with previous state-of-the-art methods on four segmentation benchmarks.
Detailed analyses are conducted to compare the performance of word encoding and
subword encoding in lattice LSTM. We also investigate the performance of
lattice LSTM structure under different circumstances and when this model works
or fails.

本文研究了一种基于格子结构的长短时记忆网络，通过与词典匹配子序列信息实现对中文分词的支持，并通过对比 Word Encoding 和 Subword Encoding 两种编码方式表明 Subword Encoding 更具优势。结果表明本文提出的模型在四个分词基准测试中表现出与先前最先进的方法相当甚至更好的结果，并对其表现和性能进行了深入分析。