7,470 research outputs found
Gradient-based Inference for Networks with Output Constraints
Practitioners apply neural networks to increasingly complex problems in
natural language processing, such as syntactic parsing and semantic role
labeling that have rich output structures. Many such structured-prediction
problems require deterministic constraints on the output values; for example,
in sequence-to-sequence syntactic parsing, we require that the sequential
outputs encode valid trees. While hidden units might capture such properties,
the network is not always able to learn such constraints from the training data
alone, and practitioners must then resort to post-processing. In this paper, we
present an inference method for neural networks that enforces deterministic
constraints on outputs without performing rule-based post-processing or
expensive discrete search. Instead, in the spirit of gradient-based training,
we enforce constraints with gradient-based inference (GBI): for each input at
test-time, we nudge continuous model weights until the network's unconstrained
inference procedure generates an output that satisfies the constraints. We
study the efficacy of GBI on three tasks with hard constraints: semantic role
labeling, syntactic parsing, and sequence transduction. In each case, the
algorithm not only satisfies constraints but improves accuracy, even when the
underlying network is state-of-the-art.Comment: AAAI 201
Parsing Argumentation Structures in Persuasive Essays
In this article, we present a novel approach for parsing argumentation
structures. We identify argument components using sequence labeling at the
token level and apply a new joint model for detecting argumentation structures.
The proposed model globally optimizes argument component types and
argumentative relations using integer linear programming. We show that our
model considerably improves the performance of base classifiers and
significantly outperforms challenging heuristic baselines. Moreover, we
introduce a novel corpus of persuasive essays annotated with argumentation
structures. We show that our annotation scheme and annotation guidelines
successfully guide human annotators to substantial agreement. This corpus and
the annotation guidelines are freely available for ensuring reproducibility and
to encourage future research in computational argumentation.Comment: Under review in Computational Linguistics. First submission: 26
October 2015. Revised submission: 15 July 201
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
This paper introduces the Multi-Genre Natural Language Inference (MultiNLI)
corpus, a dataset designed for use in the development and evaluation of machine
learning models for sentence understanding. In addition to being one of the
largest corpora available for the task of NLI, at 433k examples, this corpus
improves upon available resources in its coverage: it offers data from ten
distinct genres of written and spoken English--making it possible to evaluate
systems on nearly the full complexity of the language--and it offers an
explicit setting for the evaluation of cross-genre domain adaptation.Comment: 10 pages, 1 figures, 5 tables. v2 corrects a misreported accuracy
number for the CBOW model in the 'matched' setting. v3 adds a discussion of
the difficulty of the corpus to the analysis section. v4 is the version that
was accepted to NAACL201
Design and enhanced evaluation of a robust anaphor resolution algorithm
Syntactic coindexing restrictions are by now known to be of central importance to practical anaphor resolution approaches. Since, in particular due to structural ambiguity, the assumption of the availability of a unique syntactic reading proves to be unrealistic, robust anaphor resolution relies on techniques to overcome this deficiency.
This paper describes the ROSANA approach, which generalizes the verification of coindexing restrictions in order to make it applicable to the deficient syntactic descriptions that are provided by a robust state-of-the-art parser. By a formal evaluation on two corpora that differ with respect to text genre and domain, it is shown that ROSANA achieves high-quality robust coreference resolution. Moreover, by an in-depth analysis, it is proven that the robust implementation of syntactic disjoint reference is nearly optimal. The study reveals that, compared with approaches that rely on shallow preprocessing, the largely nonheuristic disjoint reference algorithmization opens up the possibility/or a slight improvement. Furthermore, it is shown that more significant gains are to be expected elsewhere, particularly from a text-genre-specific choice of preference strategies.
The performance study of the ROSANA system crucially rests on an enhanced evaluation methodology for coreference resolution systems, the development of which constitutes the second major contribution o/the paper. As a supplement to the model-theoretic scoring scheme that was developed for the Message Understanding Conference (MUC) evaluations, additional evaluation measures are defined that, on one hand, support the developer of anaphor resolution systems, and, on the other hand, shed light on application aspects of pronoun interpretation
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