Neural AMR: Sequence-to-Sequence Models for Parsing and Generation

Abstract

Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their application to parsing and generating text using Abstract Meaning Representation (AMR) has been limited, due to the relatively limited amount of labeled data and the nonsequential nature of the AMR graphs. We present a novel training procedure that can lift this limitation using millions of unlabeled sentences and careful preprocessing of the AMR graphs. For AMR parsing, our model achieves competitive results of 62.1 SMATCH, the current best score reported without significant use of external semantic resources. For AMR generation, our model establishes a new state-of-the-art performance of BLEU 33.8. We present extensive ablative and qualitative analysis including strong evidence that sequencebased AMR models are robust against ordering variations of graph-to-sequence conversions.

Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation

“…Delexicalisation seems to improve results, corroborating the findings from Konstas et al (2017). While Delexicalisation is harmful and Compression is beneficial for PBMT, we see the opposite in NMT models.…”

“…Besides the differences between these two MT architectures, applying preordering in the Linearisation step improves results in both cases. This seems to contradict the finding in Konstas et al (2017) regarding neural models. We conjecture that the additional training data used by Konstas et al (2017) may have decreased the gap between using and not using preordering (see also below).…”

“…NMT models frame translation as a sequence-to-sequence problem (Bahdanau et al, 2015), and have shown strong results when translating between many different language pairs (Bojar et al, 2015). Recently, Konstas et al (2017) introduce sequence-to-sequence models for parsing (text-to-AMR) and generation (AMR-totext). They use a semi-supervised training proce-dure, incorporating 20M English sentences which do not have a gold-standard AMR, thus overcoming the limited amount of data available.…”

“…To address this, Pourdamghani et al (2016) look for special realisation component for names, dates and numbers in development and test sets and add them on the training set. On the other hand, similar to Konstas et al (2017), we delexicalised these constants, replacing the original information for tags (e.g., name1 , quant1 ). A list of tag-values is kept, aiming to identifying the position and to insert the original information in the sentence after the translation step is completed.…”

“…We compare BLEU scores for some of the AMRto-text systems described in the literature (Flanigan et al, 2016;Song et al, 2016;Pourdamghani et al, 2016;Song et al, 2017;Konstas et al, 2017). Since the models of Flanigan et al (2016) and Pourdamghani et al (2016) are publicly available, we also use them with the same training data as our models.…”