A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations
In this paper, we present our multilingual dependency parser developed for the CoNLL 2017 UD Shared Task dealing with {``}Multilingual Parsing from Raw Text to Universal Dependencies{''}. Our parser extends the monolingual BIST-parser as a multi-source multilingual trainable parser. Thanks to multilingual word embeddings and one hot encodings for languages, our system can use both monolingual and multi-source training. We trained 69 monolingual language models and 13 multilingual models for the shared task. Our multilingual approach making use of different resources yield better results than the monolingual approach for 11 languages. Our system ranked 5 th and achieved 70.93 overall LAS score over the 81 test corpora (macro-averaged LAS F1 score).
Code (0)
등록된 구현이 없습니다.
Tasks
Dependency ParsingMultilingual Word EmbeddingsWord EmbeddingsSimilar Papers 제목 키워드 기반
A Fast and Lightweight System for Multilingual Dependency Parsing
We present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier. We trained our transition-based projective parser in UD version 2.0 datasets…
Dependency ParsingThe HIT-SCIR System for End-to-End Parsing of Universal Dependencies
This paper describes our system (HIT-SCIR) for the CoNLL 2017 shared task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system includes three pipelined components: \textit{tokenization}, \textit{Part…
Dependency ParsingInformation RetrievalPart-Of-Speech TaggingPOS+2Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations
We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, an…
Dependency ParsingSentenceGraph-based Dependency Parsing with Bidirectional LSTM
On the Challenges of Fully Incremental Neural Dependency Parsing
Since the popularization of BiLSTMs and Transformer-based bidirectional encoders, state-of-the-art syntactic parsers have lacked incrementality, requiring access to the whole sentence and deviating from human language pr…
Dependency ParsingSentence