3 research outputs found
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Recent studies on AMR-to-text generation often formalize the task as a
sequence-to-sequence (seq2seq) learning problem by converting an Abstract
Meaning Representation (AMR) graph into a word sequence. Graph structures are
further modeled into the seq2seq framework in order to utilize the structural
information in the AMR graphs. However, previous approaches only consider the
relations between directly connected concepts while ignoring the rich structure
in AMR graphs. In this paper we eliminate such a strong limitation and propose
a novel structure-aware self-attention approach to better modeling the
relations between indirectly connected concepts in the state-of-the-art seq2seq
model, i.e., the Transformer. In particular, a few different methods are
explored to learn structural representations between two concepts. Experimental
results on English AMR benchmark datasets show that our approach significantly
outperforms the state of the art with 29.66 and 31.82 BLEU scores on LDC2015E86
and LDC2017T10, respectively. To the best of our knowledge, these are the best
results achieved so far by supervised models on the benchmarks.Comment: Accepted by EMNLP 201
Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing
Abstract Meaning Representation (AMR) is a rooted, labeled, acyclic graph
representing the semantics of natural language. As previous works show,
although AMR is designed for English at first, it can also represent semantics
in other languages. However, they find that concepts in their predicted AMR
graphs are less specific. We argue that the misprediction of concepts is due to
the high relevance between English tokens and AMR concepts. In this work, we
introduce bilingual input, namely the translated texts as well as non-English
texts, in order to enable the model to predict more accurate concepts. Besides,
we also introduce an auxiliary task, requiring the decoder to predict the
English sequences at the same time. The auxiliary task can help the decoder
understand what exactly the corresponding English tokens are. Our proposed
cross-lingual AMR parser surpasses previous state-of-the-art parser by 10.6
points on Smatch F1 score. The ablation study also demonstrates the efficacy of
our proposed modules.Comment: Findings of ACL 202
Improving AMR Parsing with Sequence-to-Sequence Pre-training
In the literature, the research on abstract meaning representation (AMR)
parsing is much restricted by the size of human-curated dataset which is
critical to build an AMR parser with good performance. To alleviate such data
size restriction, pre-trained models have been drawing more and more attention
in AMR parsing. However, previous pre-trained models, like BERT, are
implemented for general purpose which may not work as expected for the specific
task of AMR parsing. In this paper, we focus on sequence-to-sequence (seq2seq)
AMR parsing and propose a seq2seq pre-training approach to build pre-trained
models in both single and joint way on three relevant tasks, i.e., machine
translation, syntactic parsing, and AMR parsing itself. Moreover, we extend the
vanilla fine-tuning method to a multi-task learning fine-tuning method that
optimizes for the performance of AMR parsing while endeavors to preserve the
response of pre-trained models. Extensive experimental results on two English
benchmark datasets show that both the single and joint pre-trained models
significantly improve the performance (e.g., from 71.5 to 80.2 on AMR 2.0),
which reaches the state of the art. The result is very encouraging since we
achieve this with seq2seq models rather than complex models. We make our code
and model available at https://github.com/xdqkid/S2S-AMR-Parser.Comment: Accepted by EMNLP 202