LINGUIST List 17.3779|
Wed Dec 20 2006
FYI: RTE 3-Call for Participation
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RTE 3-Call for Participation
Message 1: RTE 3-Call for Participation
From: Danilo Giampiccolo <infocelct.it>
Subject: RTE 3-Call for Participation
3rd PASCAL Textual Entailment Challenge and Resources Pool
Call for Participation
Textual entailment recognition, i.e. the task of deciding, given two texts,
whether the meaning of one text can be plausibly inferred from the other,
has gained growing popularity recently following the two previous rounds of
the PASCAL Recognizing Textual Entailment (RTE) challenge. One of the key
elements of this success is probably the fact that textual entailment may
serve as a unifying generic framework for applied modeling of semantic
inference, and captures generically a broad range of inferences that are
relevant for different application, such as Question Answering (QA),
Information Extraction (IE), Summarization, Machine Translation,
paraphrasing, and for certain types of queries in Information Retrieval
(IR). More specifically, the RTE challenge aims to focus research and
evaluation on this shared underlying semantic inference task and isolate it
from other application specific problems.
The goal of the first RTE challenge was to provide a new benchmark to test
progress in recognizing textual entailment, and to compare the achievements
of different groups. This goal has proven to be of great interest, and the
community response encouraged us to gradually extend the scope of the
original task. The second RTE Challenge built on the success of the first,
with 23 participating groups from around the world (as compared to 17 for
the first challenge). The number of participants and their contributions to
the discussion at the Workshop in April 2006 (Venice, Italy) demonstrated
that Textual Entailment is a quickly growing field of NLP research.
Already, the workshops have spawned an impressive number of publications in
major conferences, with more work in progress and about 150 downloads to
date of the RTE-2 dataset (see
http://aclweb.org/aclwiki/index.php?title=Textual_Entailment for a
comprehensive reference list).
RTE 3 follows the same basic structure of the previous campaign, in order
to facilitate the participation of newcomers and to allow ''veterans'' to
assess the improvements of their systems. Nevertheless, a couple of
innovations are introduced:
* A limited number (about 20%) of longer texts - i.e. one paragraph long -
are introduced as a first step towards addressing broader settings which
require discourse analysis.
* An RTE Resource Pool has been created as a shared central location for
resource contributors and users (see below).
TASK AND DATA DESCRIPTION
The input to the challenge task consists of pairs of text units, termed
T(ext) - the entailing text, and H(ypothesis) - the candidate entailed
text. The task consists of recognizing a directional relation between the
two text fragments, deciding whether T entails H or not. More specifically,
we say that T entails H if, typically, a human reading T would infer that H
is most likely true. System results will be compared to a human-annotated
gold-standard test set.
The following H/T pairs exemplify the task proposed in the challenge:
T: The flights begin at San Diego's Lindbergh Field in April, 2002 and
follow the Lone Eagle's 1927 flight plan to St. Louis, New York, and Paris.
H: Lindbergh began his flight from Paris to New York in 2002.
T: The world will never forget the epic flight of Charles Lindbergh across
the Atlantic from New York to Paris in May 1927, a feat still regarded as
one of the greatest in aviation history.
H: Lindbergh began his flight from New York to Paris in 1927.
T: Medical science indicates increased risks of tumors, cancer, genetic
damage and other health problems from the use of cell phones.
H: Cell phones pose health risks.
T: The available scientific reports do not show that any health problems
are associated with the use of wireless phones.
H: Cell phones pose health risks.
The development and test sets are based on multiple data sources and are
intended to be representative of typical problems encountered by applied
text understanding systems. Examples are mostly based on entailment cases
that were/were not handled successfully by existing systems, and also
include a small proportion of manually created examples that simulate an application
scenario. While most of the text pairs are drawn from the domain of
political and business news, other domains, such as sports, science, and
technology are also represented, even though any domain-specific language
is avoided and the vocabulary used is that of an average educated person.
As in RTE-2, data types corresponding to the following application areas
are used (see website for details on mapping application data to an RTE task):
a. Question Answering (QA)
b. ''Propositional'' Information Retrieval (IR)
c. Information Extraction/Relation Extraction (IE)
d. Summarization (SUM) (including PYRAMID-based data)
This year, a limited proportion of longer texts - up to a short paragraph -
are included, allowing for discourse analysis. However, the majority of
examples remain similar to those in the previous challenges, providing
pairs with relatively short texts.
In order to avoid copyright problems, data is limited to either what has
already been publicly released by official competitions or else is drawn
from freely available sources such as Wikinews and Wikipedia.
THE TEXTUAL ENTAILMENT RESOURCE POOL
One of the key conclusions at the 2nd RTE Challenge Workshop was that
entailment modeling requires vast knowledge resources that correspond to
different types of entailment reasoning. Examples of useful knowledge
include ontological and lexical relationships, paraphrases and entailment
rules, meaning entailing syntactic transformations and certain types of
world knowledge. Textual entailment systems also utilize general NLP tools
such as POS taggers, parsers and named-entity recognizers, sometimes posing
specialized requirements to such tools. With so many resources being
continuously released and improved, it can be difficult to know which
particular resource to use when developing a system. In response, RTE-3
includes a new activity for building a Textual Entailment Resource Pool,
which will serve as a portal and forum for publicizing and tracking
resources and reporting on their use.
We actively solicit both RTE participants and other members of the NLP
community who develop or use relevant resources to contribute to the
Textual Entailment Resource Pool. Contributions include links and
descriptions of relevant resources as well as informational postings
regarding resource use and accumulated experience. RTE-3 participants who
utilize such resources are expected to cite them and evaluate their impact
while the overall utility of noticeable resources will be reviewed in the
RTE-3 organizers paper, which we hope will reward contributors of useful
The Textual Entailment Resource Pool is hosted as a sub-zone of the ACL
Wiki for Computational Linguistics. The resource pool has been seeded with
a few resources, however its usefulness relies on the community's
(including your!) contributions. The Textual Entailment Resource Pool is
Note: JNLE SPECIAL ISSUE
We would like to draw attention to a preliminary announcement, which is
currently being circulated, for a special issue of the Journal for Natural
Language Engineering on Textual Entailment. The call for the special issue
is anticipated for April 2007 with submission deadline several months
later. This schedule will allow interested participants of RTE-3 to report
their recent results, following the RTE-3 workshop. The call for the
special issue will be open, covering a broader scope than exhibited in the
Development Set Release: 20 December 2006
Test Set Release: 1 March 2007
Deadline for participants' submissions: 12 March 2007
Release of individual results: 16 March 2007
Deadline for participants' reports: 2 April 2007
Camera-ready version of reports: 9 May 2007
Workshop: Early summer, 2007
(We have proposed having the RTE-3 workshop as an ACL 2007 workshop, to be
held at the end of June in Prague.)
Danilo Giampiccolo, CELCT (Trento), Italy (coordinator)
Bernardo Magnini, ITC-irst (Trento), Italy (advisor)
Ido Dagan, Bar Ilan University, Israel (supervisor and advisor)
Bill Dolan, Microsoft Research, USA
Patrick Pantel, ISI, USA (Textual Entailment Resource Pool)
The preparation and running of this challenge has been supported by the
EU-funded PASCAL Network of Excellence on Pattern Analysis, Statistical
Modeling and Computational Learning.
The data sets have been created and annotated by the Butler Hill Group
(Microsoft) and CELCT.
For registration, further information and inquiries, please visit the
CONTACT: Danilo Giampiccolo , with [RTE3] in the subject
Linguistic Field(s): Computational Linguistics
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