31 research outputs found
Computational models for multilingual negation scope detection
Negation is a common property of languages, in that there are few languages, if any,
that lack means to revert the truth-value of a statement.
A challenge to cross-lingual studies of negation lies in the fact that languages encode
and use it in different ways. Although this variation has been extensively researched in
linguistics, little has been done in automated language processing. In particular, we lack
computational models of processing negation that can be generalized across language.
We even lack knowledge of what the development of such models would require.
These models however exist and can be built by means of existing cross-lingual
resources, even when annotated data for a language other than English is not available.
This thesis shows this in the context of detecting string-level negation scope, i.e. the
set of tokens in a sentence whose meaning is affected by a negation marker (e.g. ‘not’).
Our contribution has two parts.
First, we investigate the scenario where annotated training data is available.
We show that Bi-directional Long Short Term Memory (BiLSTM) networks are
state-of-the-art models whose features can be generalized across language. We also
show that these models suffer from genre effects and that for most of the corpora we
have experimented with, high performance is simply an artifact of the annotation styles,
where negation scope is often a span of text delimited by punctuation.
Second, we investigate the scenario where annotated data is available in only one
language, experimenting with model transfer.
To test our approach, we first build NEGPAR, a parallel corpus annotated for
negation, where pre-existing annotations on English sentences have been edited and
extended to Chinese translations.
We then show that transferring a model for negation scope detection across languages
is possible by means of structured neural models where negation scope is detected on
top of a cross-linguistically consistent representation, Universal Dependencies. On
the other hand, we found cross-lingual lexical information only to help very little
with performance. Finally, error analysis shows that performance is better when a
negation marker is in the same dependency substructure as its scope and that some
of the phenomena related to negation scope requiring lexical knowledge are still not
captured correctly.
In the conclusions, we tie up the contributions of this thesis and we point future
work towards representing negation scope across languages at the level of logical form
as well
Semantic Graph Parsing with Recurrent Neural Network DAG Grammars
Semantic parses are directed acyclic graphs (DAGs), so semantic parsing
should be modeled as graph prediction. But predicting graphs presents difficult
technical challenges, so it is simpler and more common to predict the
linearized graphs found in semantic parsing datasets using well-understood
sequence models. The cost of this simplicity is that the predicted strings may
not be well-formed graphs. We present recurrent neural network DAG grammars, a
graph-aware sequence model that ensures only well-formed graphs while
sidestepping many difficulties in graph prediction. We test our model on the
Parallel Meaning Bank---a multilingual semantic graphbank. Our approach yields
competitive results in English and establishes the first results for German,
Italian and Dutch.Comment: 9 pages, to appear in EMNLP201
Evaluating Machine Translation Performance on Chinese Idioms with a Blacklist Method
Idiom translation is a challenging problem in machine translation because the
meaning of idioms is non-compositional, and a literal (word-by-word)
translation is likely to be wrong. In this paper, we focus on evaluating the
quality of idiom translation of MT systems. We introduce a new evaluation
method based on an idiom-specific blacklist of literal translations, based on
the insight that the occurrence of any blacklisted words in the translation
output indicates a likely translation error. We introduce a dataset, CIBB
(Chinese Idioms Blacklists Bank), and perform an evaluation of a
state-of-the-art Chinese-English neural MT system. Our evaluation confirms that
a sizable number of idioms in our test set are mistranslated (46.1%), that
literal translation error is a common error type, and that our blacklist method
is effective at identifying literal translation errors.Comment: Full paper accepted by LREC, 8 page