=Paper=
{{Paper
|id=Vol-1178/CLEF2012wn-QA4MRE-Clark2012
|storemode=property
|title=Recognizing Textual Entailment, QA4MRE, and Machine Reading
|pdfUrl=https://ceur-ws.org/Vol-1178/CLEF2012wn-QA4MRE-Clark2012.pdf
|volume=Vol-1178
}}
==Recognizing Textual Entailment, QA4MRE, and Machine Reading==
<pdf width="1500px">https://ceur-ws.org/Vol-1178/CLEF2012wn-QA4MRE-Clark2012.pdf</pdf>
<pre>
        Recognizing Textual Entailment, QA4MRE, and
                     Machine Reading

                                        Peter Clark
                            Vulcan Inc. Seattle, WA, 98104, USA
                                    peterc@vulcan.com




Abstract

Machine Reading remains one of the Grand Challenges of Artificial Intelligence, and
also one of the most difficult. Machine Reading requires more than just parsing a text;
it requires constructing a coherent internal model of the world which that text is
describing, including inferring facts that are implicit in the text. One basic operation
required for this is to infer or recognize Textual Entailment (TE), i.e., recognize
plausible implications of a fragment of text. In this talk I will discuss the overall goal
of Machine Reading, and the role of Recognizing Textual Entailment (RTE) within it.
I will describe how we modified an RTE system for the QA4MRE challenge, and
illustrate where it worked, where it failed but could be fixed, and where it has
fundamental limitations. I will then turn to the larger task of Machine Reading in the
context of QA4MRE, and discuss two major challenges it elicits, namely the
requirement for knowledge, and the requirement for evidential reasoning. Finally I
will offer some reflections on how these might be addressed, with the aim of moving
from sentence-matching strategies towards model-building strategies, and ultimately
towards machines that can read.

</pre>