Inference mechanisms using pragmatics and detailed knowledge for Commonsense reasoning

点击翻译按钮获取中文摘要

Commonsense reasoning (CSR) is a key component of any truly artificially intelligent agent; moreover the ability to interpret questions posed in natural language (NL) is important for many applications of AI. This project aims to investigate and make progress in this important but challenging area.A key aspect of CSR involved in question answering tasks is the ability to draw on a wealth of knowledge as well as appeal to common assumptions about the world and interpret terms in a defeasible manner. The task of structuring such knowledge is a huge challenge and is an active area of research. Well-developed cognitive theories exist involving pragmatics, dealing with common assumptions and interpretations of terms; however little is said on how to extract rules from these theories. The question then is, how can we leverage existing knowledge bases while maintaining ideas from pragmatics in order to solve question answering benchmarks?NL can be very informationally dense, allowing speakers to convey a lot of information in only a few words. There is a wealth of research relating to theories of discourse and how humans manage to deal with this semantic under specification in communication. Only with pragmatic inferences can a sentence be transformed into a formal representation of the intended proposition of the speaker. Therefore any system attempting to interpret NL should incorporate mechanisms for inferring this hidden information. Further, being able to account for these sorts of inferences can be very important in CSR for AI, in particular in the Winograd Schema Challenge (WSC). However, the difficulty of the task means that little work exists on formalising this pragmatic knowledge or integrating it into reasoning systems.Though there exist attempts to formalize some aspects of pragmatics, as pointed out by Bunt and Black some parts of pragmatics "do not enjoy a wealth of representational formalisms". One interesting avenue from the perspective of pragmatics is how to deal with prototypes. As I discuss in my paper, appealing to prototypes is an important aspect of CSR. This raises two particular questions, firstly how do we identify prototypes? Secondly, how do we identify when using prototypes is inappropriate and in these cases how do we change/loosen the definitions we are using? For instance, when one reasons about the sentence "The trophy doesn't fit into the suitcase because it is too large", what is too large? It is not necessary to worry about a precise semantic commitment for the notion of 'large', but instead to evaluate the sentence considering an interpretation of large which satisfies most notions of large. There is also the interesting case of spatial prepositions. A spatial preposition like 'on' is heavily underspecified; it can be used to denote a variety of spatial configurations - the glass on the table, the picture on the wall etc. Though it appears to have a prototypical definition - being above and in contact with - it often just denotes a salient spatial relationship between two objects and then from knowledge of the two objects and context we infer a more precise configuration. Understanding when to use a prototypical 'on' is a similar problem to 'large' - interpreting the intention of the speaker and world knowledge is required.The aim of this research is to develop systems for interpreting NL while incorporating detailed knowledge and using the principles of pragmatics to guide this. The WSC helps motivates this discussion, however formalizing the necessary aspects of reasoning to tackle the WSC and integrating them into one system is notoriously hard. Therefore, the WSC is not necessarily a suitable problem to tackle. As we will be trying to extract complex pragmatic information it makes sense to work in an area where semantic representations are well developed and understood, therefore I intend to restrict to work on problems in the spatial domain.