13,256 research outputs found
Learning Joint Semantic Parsers from Disjoint Data
We present a new approach to learning semantic parsers from multiple
datasets, even when the target semantic formalisms are drastically different,
and the underlying corpora do not overlap. We handle such "disjoint" data by
treating annotations for unobserved formalisms as latent structured variables.
Building on state-of-the-art baselines, we show improvements both in
frame-semantic parsing and semantic dependency parsing by modeling them
jointly.Comment: NAACL 201
Deep Semantic Role Labeling with Self-Attention
Semantic Role Labeling (SRL) is believed to be a crucial step towards natural
language understanding and has been widely studied. Recent years, end-to-end
SRL with recurrent neural networks (RNN) has gained increasing attention.
However, it remains a major challenge for RNNs to handle structural information
and long range dependencies. In this paper, we present a simple and effective
architecture for SRL which aims to address these problems. Our model is based
on self-attention which can directly capture the relationships between two
tokens regardless of their distance. Our single model achieves F on
the CoNLL-2005 shared task dataset and F on the CoNLL-2012 shared task
dataset, which outperforms the previous state-of-the-art results by and
F score respectively. Besides, our model is computationally
efficient, and the parsing speed is 50K tokens per second on a single Titan X
GPU.Comment: Accepted by AAAI-201
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
We present a new, efficient frame-semantic parser that labels semantic
arguments to FrameNet predicates. Built using an extension to the segmental RNN
that emphasizes recall, our basic system achieves competitive performance
without any calls to a syntactic parser. We then introduce a method that uses
phrase-syntactic annotations from the Penn Treebank during training only,
through a multitask objective; no parsing is required at training or test time.
This "syntactic scaffold" offers a cheaper alternative to traditional syntactic
pipelining, and achieves state-of-the-art performance
Transferring Semantic Roles Using Translation and Syntactic Information
Our paper addresses the problem of annotation projection for semantic role
labeling for resource-poor languages using supervised annotations from a
resource-rich language through parallel data. We propose a transfer method that
employs information from source and target syntactic dependencies as well as
word alignment density to improve the quality of an iterative bootstrapping
method. Our experiments yield a absolute labeled F-score improvement over
a standard annotation projection method
Multi-task Learning for Japanese Predicate Argument Structure Analysis
An event-noun is a noun that has an argument structure similar to a
predicate. Recent works, including those considered state-of-the-art, ignore
event-nouns or build a single model for solving both Japanese predicate
argument structure analysis (PASA) and event-noun argument structure analysis
(ENASA). However, because there are interactions between predicates and
event-nouns, it is not sufficient to target only predicates. To address this
problem, we present a multi-task learning method for PASA and ENASA. Our
multi-task models improved the performance of both tasks compared to a
single-task model by sharing knowledge from each task. Moreover, in PASA, our
models achieved state-of-the-art results in overall F1 scores on the NAIST Text
Corpus. In addition, this is the first work to employ neural networks in ENASA.Comment: 10 pages; NAACL 201
Linguistically-Informed Self-Attention for Semantic Role Labeling
Current state-of-the-art semantic role labeling (SRL) uses a deep neural
network with no explicit linguistic features. However, prior work has shown
that gold syntax trees can dramatically improve SRL decoding, suggesting the
possibility of increased accuracy from explicit modeling of syntax. In this
work, we present linguistically-informed self-attention (LISA): a neural
network model that combines multi-head self-attention with multi-task learning
across dependency parsing, part-of-speech tagging, predicate detection and SRL.
Unlike previous models which require significant pre-processing to prepare
linguistic features, LISA can incorporate syntax using merely raw tokens as
input, encoding the sequence only once to simultaneously perform parsing,
predicate detection and role labeling for all predicates. Syntax is
incorporated by training one attention head to attend to syntactic parents for
each token. Moreover, if a high-quality syntactic parse is already available,
it can be beneficially injected at test time without re-training our SRL model.
In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art
performance for a model using predicted predicates and standard word
embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art
on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in
error. On ConLL-2012 English SRL we also show an improvement of more than 2.5
F1. LISA also out-performs the state-of-the-art with contextually-encoded
(ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on
out-of-domain text.Comment: In Conference on Empirical Methods in Natural Language Processing
(EMNLP). Brussels, Belgium. October 201
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
We present a transition-based parser that jointly produces syntactic and
semantic dependencies. It learns a representation of the entire algorithm
state, using stack long short-term memories. Our greedy inference algorithm has
linear time, including feature extraction. On the CoNLL 2008--9 English shared
tasks, we obtain the best published parsing performance among models that
jointly learn syntax and semantics.Comment: Proceedings of CoNLL 2016; 13 pages, 5 figure
Keypoint Based Weakly Supervised Human Parsing
Fully convolutional networks (FCN) have achieved great success in human
parsing in recent years. In conventional human parsing tasks, pixel-level
labeling is required for guiding the training, which usually involves enormous
human labeling efforts. To ease the labeling efforts, we propose a novel weakly
supervised human parsing method which only requires simple object keypoint
annotations for learning. We develop an iterative learning method to generate
pseudo part segmentation masks from keypoint labels. With these pseudo masks,
we train an FCN network to output pixel-level human parsing predictions.
Furthermore, we develop a correlation network to perform joint prediction of
part and object segmentation masks and improve the segmentation performance.
The experiment results show that our weakly supervised method is able to
achieve very competitive human parsing results. Despite our method only uses
simple keypoint annotations for learning, we are able to achieve comparable
performance with fully supervised methods which use the expensive pixel-level
annotations
Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments
Implicit semantic role labeling (iSRL) is the task of predicting the semantic
roles of a predicate that do not appear as explicit arguments, but rather
regard common sense knowledge or are mentioned earlier in the discourse. We
introduce an approach to iSRL based on a predictive recurrent neural semantic
frame model (PRNSFM) that uses a large unannotated corpus to learn the
probability of a sequence of semantic arguments given a predicate. We leverage
the sequence probabilities predicted by the PRNSFM to estimate selectional
preferences for predicates and their arguments. On the NomBank iSRL test set,
our approach improves state-of-the-art performance on implicit semantic role
labeling with less reliance than prior work on manually constructed language
resources.Comment: IJCNLP 201
A Span Selection Model for Semantic Role Labeling
We present a simple and accurate span-based model for semantic role labeling
(SRL). Our model directly takes into account all possible argument spans and
scores them for each label. At decoding time, we greedily select higher scoring
labeled spans. One advantage of our model is to allow us to design and use
span-level features, that are difficult to use in token-based BIO tagging
approaches. Experimental results demonstrate that our ensemble model achieves
the state-of-the-art results, 87.4 F1 and 87.0 F1 on the CoNLL-2005 and 2012
datasets, respectively.Comment: Accepted by EMNLP 201
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