Commonsense Reasoning
commonsense reasoning
N/A
Applications are invited for a Postdoctoral Research Associate 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 neuro-symbolic reasoning strategies to fill the gap between what is explicitly stated in a story and what a human reader would infer by “reading between the lines”.
Improving Language Understanding by Generative Pre Training
Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classification. Although large unlabeled text corpora are abundant, labeled data for learning these specific tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task. In contrast to previous approaches, we make use of task-aware input transformations during fine-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures specifically crafted for each task, significantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI).
Models of Core Knowledge (Physics, Really)
Even young children seem to have an early understanding of the world around them, and the people in it. Before children can reliably say "ball", "wall", or "Saul", they expect balls to not go through walls, and for Saul to go right for a ball (if there's no wall). What is the formal conceptual structure underlying this commonsense reasoning about objects and agents? I will raise several possibilities for models underlying core intuitive physics as a way of talking about models of core knowledge and intuitive theories more generally. In particular, I will present some recent ML work trying to capture early expectations about object solidly, cohesion, and permanence, that relies on a rough-derendering approach.