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<p class="p1"><span class="s1"><b>Location</b>: Cardiff, UK</span><o:p></o:p></p>
<p class="p1"><span class="s1"><b>Deadline for applications</b>: 31st January 2023</span><o:p></o:p></p>
<p class="p1"><span class="s1"><b>Start date</b>: as soon as possible</span><o:p></o:p></p>
<p class="p1"><span class="s1"><b>Duration</b>: 30 months</span><o:p></o:p></p>
<p class="p1"><span class="s1"><b>Keywords</b>: natural language processing, neurosymbolic AI, graph neural networks, commonsense reasoning</span><o:p></o:p></p>
<p class="p2"><o:p> </o:p></p>
<p class="p1"><span class="s1"><b>Details about the post</b></span><o:p></o:p></p>
<p class="p1"><span class="s1">Applications are invited for a Research Associate post in the Cardiff University School of Computer Science & Informatics, to work on the EPSRC Open Fellowship project ReStoRe (Reasoning about Structured Story Representations),
which is focused on story-level language understanding. The overall aim of this project is to develop methods for learning graph-structured representations of stories. For this post, the specific focus will be on developing common sense reasoning strategies,
based on graph neural networks, to fill the gap between what is explicitly stated in a story and what a human reader would infer by “reading between the lines”.</span><span class="apple-converted-space">
</span><span class="s1">More details about the post and instructions on how to apply are available here:</span><o:p></o:p></p>
<p class="p2"><o:p> </o:p></p>
<p class="p3"><a href="https://www.jobs.ac.uk/job/CWM298/research-associate">https://www.jobs.ac.uk/job/CWM298/research-associate</a><o:p></o:p></p>
<p class="p2"><o:p> </o:p></p>
<p class="p1"><span class="s1"><b>Background about the ReStoRe project</b></span><o:p></o:p></p>
<p class="p1"><span class="s1">When we read a story as a human, we build up a mental model of what is described. Such mental models are crucial for reading comprehension. They allow us to relate the story to our earlier experiences, to make inferences that
require combining information from different sentences, and to interpret ambiguous sentences correctly. Crucially, mental models capture more information than what is literally mentioned in the story. They are representations of the situations that are described,
rather than the text itself, and they are constructed by combining the story text with our commonsense understanding of how the world works.</span><o:p></o:p></p>
<p class="p2"><o:p> </o:p></p>
<p class="p1"><span class="s1">The field of Natural Language Processing (NLP) has made rapid progress in the last few years, but the focus has largely been on sentence-level representations. Stories, such as news articles, social media posts or medical case
reports, are essentially modelled as collections of sentences. As a result, current systems struggle with the ambiguity of language, since the correct interpretation of a word or sentence can often only be inferred by taking its broader story context into
account. They are also severely limited in their ability to solve problems where information from different sentences needs to be combined. As a final example, current systems struggle to identify correspondences between related stories (e.g. different news
articles about the same event), especially if they are written from a different perspective.</span><o:p></o:p></p>
<p class="p2"><o:p> </o:p></p>
<p class="p1"><span class="s1">To address these fundamental challenges, we need a method to learn story-level representations that can act as an analogue to mental models. Intuitively, there are two steps involved in learning such story representations: first
we need to model what is literally mentioned in the story, and then we need some form of commonsense reasoning to fill in the gaps. In practice, however, these two steps are closely interrelated: interpreting what is mentioned in the story requires a model
of the story context, but constructing this model requires an interpretation of what is mentioned.</span><span class="apple-converted-space"> </span><o:p></o:p></p>
<p class="p2"><o:p> </o:p></p>
<p class="p1"><span class="s1">The solution that is proposed in this fellowship is based on representations called story graphs. These story graphs encode the events that occur, the entities involved, and the relationships that hold between these entities and
events. A story can then be viewed as an incomplete specification of a story graph, similar to how a symbolic knowledge base corresponds to an incomplete specification of a possible world. The proposed framework will allow us to reason about textual information
in a principled way. It will lead to significant improvements in NLP tasks where a commonsense understanding is required of the situations that are described, or where information from multiple sentences or documents needs to be combined. It will furthermore
enable a step change in applications that directly rely on structured text representations, such as situational understanding, information retrieval systems for the legal, medical and news domains, and tools for inferring business insights from news stories
and social media feeds.</span><o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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