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 …

Almost all current dependency parsers classify based on millions of sparse indicator
features. Not only do these features generalize poorly, but the cost of feature computation …

We propose a technique for learning representations of parser states in transition-based
dependency parsers. Our primary innovation is a new control structure for sequence-to …

We present extensions to a continuous-state dependency parsing method that makes it
applicable to morphologically rich languages. Starting with a high-performance transition …

Recent years have seen a paradigm shift in NLP towards using pretrained language models
({PLM}) for a wide range of tasks. However, there are many difficult design decisions to …

Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich
contextual syntactic and even semantic meanings. This paper makes the first attempt to …

We assume that the tokenization of a sentence is fixed and known at parsing time. That is to
say that dependency parsers will always operate on a pre-tokenized input and are not …

Syntactic and semantic parsing has been investigated for decades, which is one primary
topic in the natural language processing community. This article aims for a brief survey on …

This paper presents a sequence to sequence (seq2seq) dependency parser by directly
predicting the relative position of head for each given word, which therefore results in a truly …

Transition-based dependency parsers generally use heuristic decoding algorithms but can
accommodate arbitrarily rich feature representations. In this paper, we show that we can …