10 research outputs found
An AMR Aligner Tuned by Transition-based Parser
In this paper, we propose a new rich resource enhanced AMR aligner which
produces multiple alignments and a new transition system for AMR parsing along
with its oracle parser. Our aligner is further tuned by our oracle parser via
picking the alignment that leads to the highest-scored achievable AMR graph.
Experimental results show that our aligner outperforms the rule-based aligner
in previous work by achieving higher alignment F1 score and consistently
improving two open-sourced AMR parsers. Based on our aligner and transition
system, we develop a transition-based AMR parser that parses a sentence into
its AMR graph directly. An ensemble of our parsers with only words and POS tags
as input leads to 68.4 Smatch F1 score.Comment: EMNLP201
AMR Parsing via Graph-Sequence Iterative Inference
We propose a new end-to-end model that treats AMR parsing as a series of dual
decisions on the input sequence and the incrementally constructed graph. At
each time step, our model performs multiple rounds of attention, reasoning, and
composition that aim to answer two critical questions: (1) which part of the
input \textit{sequence} to abstract; and (2) where in the output \textit{graph}
to construct the new concept. We show that the answers to these two questions
are mutually causalities. We design a model based on iterative inference that
helps achieve better answers in both perspectives, leading to greatly improved
parsing accuracy. Our experimental results significantly outperform all
previously reported \textsc{Smatch} scores by large margins. Remarkably,
without the help of any large-scale pre-trained language model (e.g., BERT),
our model already surpasses previous state-of-the-art using BERT. With the help
of BERT, we can push the state-of-the-art results to 80.2\% on LDC2017T10 (AMR
2.0) and 75.4\% on LDC2014T12 (AMR 1.0).Comment: ACL202
Levi Graph AMR Parser using Heterogeneous Attention
Coupled with biaffine decoders, transformers have been effectively adapted to
text-to-graph transduction and achieved state-of-the-art performance on AMR
parsing. Many prior works, however, rely on the biaffine decoder for either or
both arc and label predictions although most features used by the decoder may
be learned by the transformer already. This paper presents a novel approach to
AMR parsing by combining heterogeneous data (tokens, concepts, labels) as one
input to a transformer to learn attention, and use only attention matrices from
the transformer to predict all elements in AMR graphs (concepts, arcs, labels).
Although our models use significantly fewer parameters than the previous
state-of-the-art graph parser, they show similar or better accuracy on AMR 2.0
and 3.0.Comment: Accepted in IWPT 2021: The 17th International Conference on Parsing
Technologie
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Our work involves enriching the Stack-LSTM transition-based AMR parser
(Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning
and rewarding the Smatch score of sampled graphs. In addition, we also combined
several AMR-to-text alignments with an attention mechanism and we supplemented
the parser with pre-processed concept identification, named entities and
contextualized embeddings. We achieve a highly competitive performance that is
comparable to the best published results. We show an in-depth study ablating
each of the new components of the parserComment: Accepted as short paper at ACL 201
AMR Parsing as Sequence-to-Graph Transduction
We propose an attention-based model that treats AMR parsing as
sequence-to-graph transduction. Unlike most AMR parsers that rely on
pre-trained aligners, external semantic resources, or data augmentation, our
proposed parser is aligner-free, and it can be effectively trained with limited
amounts of labeled AMR data. Our experimental results outperform all previously
reported SMATCH scores, on both AMR 2.0 (76.3% F1 on LDC2017T10) and AMR 1.0
(70.2% F1 on LDC2014T12).Comment: Accepted at ACL 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
Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments
We present algorithms for aligning components of Abstract Meaning
Representation (AMR) graphs to spans in English sentences. We leverage
unsupervised learning in combination with heuristics, taking the best of both
worlds from previous AMR aligners. Our unsupervised models, however, are more
sensitive to graph substructures, without requiring a separate syntactic parse.
Our approach covers a wider variety of AMR substructures than previously
considered, achieves higher coverage of nodes and edges, and does so with
higher accuracy. We will release our LEAMR datasets and aligner for use in
research on AMR parsing, generation, and evaluation.Comment: ACL 2021 Camera-read
Structural Information Preserving for Graph-to-Text Generation
The task of graph-to-text generation aims at producing sentences that
preserve the meaning of input graphs. As a crucial defect, the current
state-of-the-art models may mess up or even drop the core structural
information of input graphs when generating outputs. We propose to tackle this
problem by leveraging richer training signals that can guide our model for
preserving input information. In particular, we introduce two types of
autoencoding losses, each individually focusing on different aspects (a.k.a.
views) of input graphs. The losses are then back-propagated to better calibrate
our model via multi-task training. Experiments on two benchmarks for
graph-to-text generation show the effectiveness of our approach over a
state-of-the-art baseline. Our code is available at
\url{http://github.com/Soistesimmer/AMR-multiview}.Comment: ACL 202
Semantically Constrained Multilayer Annotation: The Case of Coreference
We propose a coreference annotation scheme as a layer on top of the Universal
Conceptual Cognitive Annotation foundational layer, treating units in
predicate-argument structure as a basis for entity and event mentions. We argue
that this allows coreference annotators to sidestep some of the challenges
faced in other schemes, which do not enforce consistency with
predicate-argument structure and vary widely in what kinds of mentions they
annotate and how. The proposed approach is examined with a pilot annotation
study and compared with annotations from other schemes.Comment: Accepted to The First International Workshop on Designing Meaning
Representations (DMR), 2019 (in conjunction with ACL 2019
Core Semantic First: A Top-down Approach for AMR Parsing
We introduce a novel scheme for parsing a piece of text into its Abstract
Meaning Representation (AMR): Graph Spanning based Parsing (GSP). One novel
characteristic of GSP is that it constructs a parse graph incrementally in a
top-down fashion. Starting from the root, at each step, a new node and its
connections to existing nodes will be jointly predicted. The output graph spans
the nodes by the distance to the root, following the intuition of first
grasping the main ideas then digging into more details. The \textit{core
semantic first} principle emphasizes capturing the main ideas of a sentence,
which is of great interest. We evaluate our model on the latest AMR sembank and
achieve the state-of-the-art performance in the sense that no heuristic graph
re-categorization is adopted. More importantly, the experiments show that our
parser is especially good at obtaining the core semantics.Comment: EMNLP201