210 research outputs found
Separating Dependency from Constituency in a Tree Rewriting System
In this paper we present a new tree-rewriting formalism called Link-Sharing
Tree Adjoining Grammar (LSTAG) which is a variant of synchronous TAGs. Using
LSTAG we define an approach towards coordination where linguistic dependency is
distinguished from the notion of constituency. Such an approach towards
coordination that explicitly distinguishes dependencies from constituency gives
a better formal understanding of its representation when compared to previous
approaches that use tree-rewriting systems which conflate the two issues.Comment: 7 pages, 6 Postscript figures, uses fullname.st
Incremental Parser Generation for Tree Adjoining Grammars
This paper describes the incremental generation of parse tables for the
LR-type parsing of Tree Adjoining Languages (TALs). The algorithm presented
handles modifications to the input grammar by updating the parser generated so
far. In this paper, a lazy generation of LR-type parsers for TALs is defined in
which parse tables are created by need while parsing. We then describe an
incremental parser generator for TALs which responds to modification of the
input grammar by updating parse tables built so far.Comment: 12 pages, 12 Postscript figures, uses fullname.st
Coordination in Tree Adjoining Grammars: Formalization and Implementation
In this paper we show that an account for coordination can be constructed
using the derivation structures in a lexicalized Tree Adjoining Grammar (LTAG).
We present a notion of derivation in LTAGs that preserves the notion of fixed
constituency in the LTAG lexicon while providing the flexibility needed for
coordination phenomena. We also discuss the construction of a practical parser
for LTAGs that can handle coordination including cases of non-constituent
coordination.Comment: 6 pages, 16 Postscript figures, uses colap.sty. To appear in the
proceedings of COLING 199
SpEL: Structured Prediction for Entity Linking
Entity linking is a prominent thread of research focused on structured data
creation by linking spans of text to an ontology or knowledge source. We
revisit the use of structured prediction for entity linking which classifies
each individual input token as an entity, and aggregates the token predictions.
Our system, called SpEL (Structured prediction for Entity Linking) is a
state-of-the-art entity linking system that uses some new ideas to apply
structured prediction to the task of entity linking including: two refined
fine-tuning steps; a context sensitive prediction aggregation strategy;
reduction of the size of the model's output vocabulary, and; we address a
common problem in entity-linking systems where there is a training vs.
inference tokenization mismatch. Our experiments show that we can outperform
the state-of-the-art on the commonly used AIDA benchmark dataset for entity
linking to Wikipedia. Our method is also very compute efficient in terms of
number of parameters and speed of inference
Interrogating the Explanatory Power of Attention in Neural Machine Translation
Attention models have become a crucial component in neural machine
translation (NMT). They are often implicitly or explicitly used to justify the
model's decision in generating a specific token but it has not yet been
rigorously established to what extent attention is a reliable source of
information in NMT. To evaluate the explanatory power of attention for NMT, we
examine the possibility of yielding the same prediction but with counterfactual
attention models that modify crucial aspects of the trained attention model.
Using these counterfactual attention mechanisms we assess the extent to which
they still preserve the generation of function and content words in the
translation process. Compared to a state of the art attention model, our
counterfactual attention models produce 68% of function words and 21% of
content words in our German-English dataset. Our experiments demonstrate that
attention models by themselves cannot reliably explain the decisions made by a
NMT model.Comment: Accepted at the 3rd Workshop on Neural Generation and Translation
(WNGT 2019) held at EMNLP-IJCNLP 2019 (Camera ready
An Easily Extensible HMM Word Aligner
In this paper, we present a new word aligner with built-in support for alignment types, as well as comparisons between various models and existing aligner systems. It is an open source software that can be easily extended to use models of users\u27 own design. We expect it to suffice the academics as well as scientists working in the industry to do word alignment, as well as experimenting on their own new models. Here in the present paper, the basic designs and structures will be introduced. Examples and demos of the system are also provide
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