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Evaluation Order Effects in Dynamic Continuized CCG: From Negative Polarity Items to Balanced Punctuation
Combinatory Categorial Grammar\u27s (CCG; Steedman, 2000) flexible
treatment of word order and constituency enable it to employ a compact
lexicon, an important factor in its successful application to a range
of NLP problems. However, its word order flexibility can be
problematic for linguistic phenomena where linear order plays a key
role. In this paper, we show that the enhanced control over
evaluation order afforded by Continuized CCG (Barker & Shan, 2014)
makes it possible to not only implement an improved analysis of
negative polarity items in Dynamic Continuized CCG (White et al.,
2017) but also to develop an accurate treatment of balanced
punctuation
The Grail theorem prover: Type theory for syntax and semantics
As the name suggests, type-logical grammars are a grammar formalism based on
logic and type theory. From the prespective of grammar design, type-logical
grammars develop the syntactic and semantic aspects of linguistic phenomena
hand-in-hand, letting the desired semantics of an expression inform the
syntactic type and vice versa. Prototypical examples of the successful
application of type-logical grammars to the syntax-semantics interface include
coordination, quantifier scope and extraction.This chapter describes the Grail
theorem prover, a series of tools for designing and testing grammars in various
modern type-logical grammars which functions as a tool . All tools described in
this chapter are freely available
Research in the Language, Information and Computation Laboratory of the University of Pennsylvania
This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania.
It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition.
Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html
In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report.
The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn
Probing Natural Language Inference Models through Semantic Fragments
Do state-of-the-art models for language understanding already have, or can
they easily learn, abilities such as boolean coordination, quantification,
conditionals, comparatives, and monotonicity reasoning (i.e., reasoning about
word substitutions in sentential contexts)? While such phenomena are involved
in natural language inference (NLI) and go beyond basic linguistic
understanding, it is unclear the extent to which they are captured in existing
NLI benchmarks and effectively learned by models. To investigate this, we
propose the use of semantic fragments---systematically generated datasets that
each target a different semantic phenomenon---for probing, and efficiently
improving, such capabilities of linguistic models. This approach to creating
challenge datasets allows direct control over the semantic diversity and
complexity of the targeted linguistic phenomena, and results in a more precise
characterization of a model's linguistic behavior. Our experiments, using a
library of 8 such semantic fragments, reveal two remarkable findings: (a)
State-of-the-art models, including BERT, that are pre-trained on existing NLI
benchmark datasets perform poorly on these new fragments, even though the
phenomena probed here are central to the NLI task. (b) On the other hand, with
only a few minutes of additional fine-tuning---with a carefully selected
learning rate and a novel variation of "inoculation"---a BERT-based model can
master all of these logic and monotonicity fragments while retaining its
performance on established NLI benchmarks.Comment: AAAI camera-ready versio
CLiFF Notes: Research in the Language Information and Computation Laboratory of The University of Pennsylvania
This report takes its name from the Computational Linguistics Feedback Forum (CLIFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science, Psychology, and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. With 48 individual contributors and six projects represented, this is the largest LINC Lab collection to date, and the most diverse
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