Abstract
Sequence-to-sequence constituent parsing requires a linearization to represent trees as sequences. Top-down tree linearizations, which can be based on brackets or shift-reduce actions, have achieved the best accuracy to date. In this paper, we show that these results can be improved by using an in-order linearization instead. Based on this observation, we implement an enriched in-order shift-reduce linearization inspired by Vinyals et al. (2015)’s approach, achieving the best accuracy to date on the English PTB dataset among fully-supervised single-model sequence-to-sequence constituent parsers. Finally, we apply deterministic attention mechanisms to match the speed of state-of-the-art transition-based parsers, thus showing that sequence-to-sequence models can match them, not only in accuracy, but also in speed.
Highlights
Sequence-to-sequence neural architectures have proved useful in several NLP tasks, with remarkable success in some of them such as machine translation, but they lag behind the state of the art in others
Seq2seq models still need to improve to be competitive in accuracy and efficiency with their main competitors: transition-based constituent parsers (Dyer et al, 2016; Liu and Zhang, 2017b; Fernandez-Gonzalez and Gomez-Rodrıguez, 2019)
Transitions are the same as in the top-down algorithm, but the effect of applying a Reduce transition is different: it pops all elements from the stack until the first non-terminal node is found, which is popped together with the preceding element in the stack to build a new constituent with all of them as children of the non-terminal node.3. This algorithm pushed state-of-the-art accuracies in shift-reduce constituent parsing; and, as we show in Section 4, it can be succesfully applied as a linearization method for seq2seq constituent parsing
Summary
Sequence-to-sequence (seq2seq) neural architectures have proved useful in several NLP tasks, with remarkable success in some of them such as machine translation, but they lag behind the state of the art in others. Vinyals et al (2015) laid the first stone in seq2seq constituent parsing, proposing a linearization of phrase-structure trees as bracketed sequences following a top-down strategy, which can be predicted from the input sequence of words by any off-the-shelf seq2seq framework. While this approach is very simple, its accuracy and efficiency are significantly behind the state of the art in the fully-supervised single-model scenario. Contributions In this paper, we advance the understanding of linearizations for seq2seq parsing, and improve the state of the art, as follows: (1) we show that the superiority of a transition-based top-down linearization over a bracketing-based one observed by Liu and Zhang (2017a) does not hold when both are tested under the same framework. The result is a seq2seq parser that, for the first time, matches the speed and accuracy of transition-based parsers implemented under the same neural framework. (5) Using the neural framework of Dyer et al (2015) as testing ground, we perform a homogeneous comparison among different seq2seq linearizations and widely-known transition-based parsers
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