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We present a generative model for the unsupervised learning of dependency structures. We also describe the multiplicative combination of this dependency model with a model of linear constituency. The product …...

AbstractIn this paper we present LX-Parser, a probabilistic, robust constituency parser for Portuguese. This parser achieves ca. 88% f-score in the labeled bracketing task, thus reaching a state-of-the-art performance score …...

We investigate two aspects of the empirical behavior of paired significance tests for NLP systems. First, when one system appears to outperform another, how does significance level relate in practice …...

Parsing accuracy using efficient greedy transi- tion systems has improved dramatically in re- cent years thanks to neural networks. Despite striking results in dependency parsing, how- ever, neural models have …...

AbstractGenerative neural models have recently achieved state-of-the-art results for constituency parsing. However, without a feasible search procedure, their use has so far been limited to reranking the output of external …...

AbstractWe show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no pre-training, fastText, ELMo, …...

AbstractThe Penn Treebank (PTB) represents syntactic structures as graphs due to nonlocal dependencies. This paper proposes a method that approximates PTB graph-structured representations by trees. By our approximation method, we …...

AbstractWe use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions. To do so, we cast the problem as multitask learning (MTL). First, …...

AbstractWe propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks. Specifically, our model estimates the likelihood of a span being a legitimate …...

AbstractConstituency parsing is a fundamental and important task for natural language understanding, where a good representation of contextual information can help this task. N-grams, which is a conventional type of …...

AbstractWe analyze several recent unsupervised constituency parsing models, which are tuned with respect to the parsing F1 score on the Wall Street Journal (WSJ) development set (1,700 sentences). We introduce …...

AbstractWe introduce a generic seq2seq parsing framework that casts constituency parsing problems (syntactic and discourse parsing) into a series of conditional splitting decisions. Our parsing model estimates the conditional probability …...

AbstractNaturally-occurring bracketings, such as answer fragments to natural language questions and hyperlinks on webpages, can reflect human syntactic intuition regarding phrasal boundaries. Their availability and approximate correspondence to syntax make …...

We propose two fast neural combinatory mod- els for constituency parsing: binary and multi- branching. Our models decompose the bottom- up parsing process into 1) classification of tags, labels, and …...

Attention mechanisms and linear-chain con- ditional random field (CRF) have been ap- plied to constituency parsing, and the achieved results are phenomenal. While self-attention and label attention layers (LAL) have …...

AbstractConstituency parsing and nested named entity recognition (NER) are similar tasks since they both aim to predict a collection of nested and non-crossing spans. In this work, we cast nested …...

AbstractIn recent years, top-down neural models have achieved significant success in text-level discourse parsing. Nevertheless, they still suffer from the top-down error propagation issue, especially when the performance on the …...

AbstractThis paper considers the task of parsing low-resource languages in a scenario where parallel English data and also a limited seed of annotated sentences in the target language are available, …...

AbstractRecent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task. Despite their high accuracy in identifying low-level structures, prior arts tend …...

AbstractData augmentation is an effective approach to tackle over-fitting. Many previous works have proposed different data augmentations strategies for NLP, such as noise injection, word replacement, back-translation etc. Though effective, …...