Experiments in Recognising Textual Entailment
This repository contains python code for experiments relating to RTE. At the moment, the main thing of interest here is the Guardian Headlines Entailment Training Dataset.
What is Textual Entailment?
Recognising Textual Entailment (RTE) is the task of determining, given two sentences whether the first (called the "text") entails or implies the second (called the "hypothesis"). In this task, the term "entailment" is generally fairly loosely defined, and datasets are normally built by asking human subjects whether they consider entailment to hold.
RTE as a field of study really kicked off with the Recognising Textual Entailment Challenge.
Why Textual Entailment?
Textual Entailment is a generalisation of many tasks in natural language processing. If you have a system that is good at recognising textual entailment, it should be easier to build good systems for information retrieval, question answering, paraphrase recognition, information extraction and summarisation.
The need for automatically constructed datasets
Because of the expense of manually constructing entailment datasets, they are normally fairly small, which means machine learning approaches to the task perform sub-optimally.
As Hickl et al. showed, automatically constructed datasets can improve the performance of systems using machine learning by up to ten percent.
The Guardian Headlines Entailment Training Dataset
The dataset consists of around 32,000 pairs of sentences (16,233 for which entailment does hold and 16,249 for which it doesn't) automatically extracted from The Guardian newspaper using their API. We follow a similar methodology to Hickl et al.: we treat headlines as being entailed by the first sentence, and adjacent sentences in the remainder of the text as non-entailing.
Each pair must pass a number of criteria (arrived at in a fairly ad-hoc manner):
- The pair must share a named entity (or part of a named entity) in common (as required by Hickl et al.)
- Each sentence in the pair must not be too short or long
- Each sentence must not contain too many new line characters
- Each sentence must contain an even number of quotation marks
The source of the data is 78,696 Guardian articles from 1st January 2004 onwards obtained through the Guardian API.
No analysis has yet been performed on the dataset, so use it at your own risk! The intention is eventually to manually analyse a sample of the data.
The data is in XML format and is the same as the RTE-1 dataset:
<pair id="1" value="TRUE"> <t> Italian authorities yesterday blocked mail from Bologna addressed to EU institutions as they tried to end a letter-bomb assault that has been aimed at European targets. </t> <h> Mail block to catch EU book bombs </h> </pair>
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