RTE-7: Recognizing Textual Entailment (7th round)

Introduction

Given two text fragments called ‘Text’ and ‘Hypothesis’, Textual Entailment Recognition is the task of determining whether the meaning of the Hypothesis is entailed (can be inferred) from the Text. The goal of the first RTE Challenge was to provide the NLP community with a benchmark to test progress in recognizing textual entailment, and to compare the achievements of different groups. Since its inception in 2004, the RTE Challenges have promoted research in textual entailment recognition as a generic task that captures major semantic inference needs across many natural language processing applications, such as Question Answering (QA), Information Retrieval (IR), Information Extraction (IE), and multi-document Summarization.

After the first three highly successful PASCAL RTE Challenges, RTE became a track at the 2008 Text Analysis Conference, which brought it together with communities working on NLP applications. The interaction has provided the opportunity to apply RTE systems to specific applications and to move the RTE task towards more realistic application settings.

RTE-7 pursues the direction taken in RTE-6, focusing on textual entailment in context, where the entailment decision draws on the larger context available in the targeted application settings.

RTE-7 Tasks

The RTE-7 tasks focus on recognizing textual entailment in two application settings: Summarization and Knowledge Base Population.

1. Main Task (Summarization setting): Given a corpus and a set of “candidate” sentences retrieved by Lucene from that corpus, RTE systems are required to identify all the sentences from among the candidate sentences that entail a given Hypothesis. The RTE-7 Main Task is based on the TAC Update Summarization Task. In the Update Summarization Task, each topic contains two sets of documents (“A” and “B”), where all the “A” documents chronologically precede all the “B” documents. An RTE-7 Main Task “corpus” consists of 10 “A” documents, while Hypotheses are taken from sentences in the “B” documents.
2. Novelty Detection Subtask (Summarization setting): In the Novelty Detection variant of the Main Task, systems are required to judge if the information contained in each H (based on text snippets from B summaries) is novel with respect to the information contained in the A documents related to the same topic. If entailing sentences are found for a given H, it means that the content of H is not new; if no entailing sentences are detected, it means that information contained in the H is novel.
3. KBP Validation Task (Knowledge Base Population setting): Based on the TAC Knowledge Base Population (KBP) Slot-Filling task, the KBP validation task is to determine whether a given relation (Hypothesis) is supported in an associated document (Text). Each slot fill that is proposed by a system for the KBP Slot-Filling task would create one evaluation item for the RTE-KBP Validation Task: The Hypothesis would be a simple sentence created from the slot fill, while the Text would be the source document that was cited as supporting the slot fill.

Schedule

Mailing List

The mailing list for the RTE Track is rte@nist.gov. The list is used to discuss and define the task guidelines for the track, as well as for general discussion related to textual entailment and its evaluation. To subscribe, send a message to listproc@email.nist.gov such that the body consists of the line:
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HELP

Organizing Committee

Luisa Bentivogli, CELCT and FBK, Italy
Peter Clark, Vulcan Inc., USA
Ido Dagan, Bar Ilan University, Israel
Hoa Trang Dang, NIST, USA
Danilo Giampiccolo, CELCT, Italy