A Crowdsourced Frame Disambiguation Corpus with Ambiguity

We present a resource for the task of FrameNet semantic frame disambiguation of over 5,000 word-sentence pairs from the Wikipedia corpus. The annotations were collected using a novel crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. In contrast to the typical approach of attributing the best single frame to each word, we provide a list of frames with disagreement-based scores that express the confidence with which each frame applies to the word. This is based on the idea that inter-annotator disagreement is at least partly caused by ambiguity that is inherent to the text and frames. We have found many examples where the semantics of individual frames overlap sufficiently to make them acceptable alternatives for interpreting a sentence. We have argued that ignoring this ambiguity creates an overly arbitrary target for training and evaluating natural language processing systems - if humans cannot agree, why would we expect the correct answer from a machine to be any different? To process this data we also utilized an expanded lemma-set provided by the Framester system, which merges FN with WordNet to enhance coverage. Our dataset includes annotations of 1,000 sentence-word pairs whose lemmas are not part of FN. Finally we present metrics for evaluating frame disambiguation systems that account for ambiguity.Comment: Accepted to NAACL-HLT201

Hi, how can I help you?: Automating enterprise IT support help desks

Question answering is one of the primary challenges of natural language understanding. In realizing such a system, providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation. The different methods explored in the literature can be broadly classified into three categories namely: 1) classification based, 2) knowledge graph based and 3) retrieval based. Individually, none of them address the need of an enterprise wide assistance system for an IT support and maintenance domain. In this domain the variance of answers is large ranging from factoid to structured operating procedures; the knowledge is present across heterogeneous data sources like application specific documentation, ticket management systems and any single technique for a general purpose assistance is unable to scale for such a landscape. To address this, we have built a cognitive platform with capabilities adopted for this domain. Further, we have built a general purpose question answering system leveraging the platform that can be instantiated for multiple products, technologies in the support domain. The system uses a novel hybrid answering model that orchestrates across a deep learning classifier, a knowledge graph based context disambiguation module and a sophisticated bag-of-words search system. This orchestration performs context switching for a provided question and also does a smooth hand-off of the question to a human expert if none of the automated techniques can provide a confident answer. This system has been deployed across 675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201

Visual world studies of conversational perspective taking: similar findings, diverging interpretations

Visual-world eyetracking greatly expanded the potential for insight into how listeners access and use common ground during situated language comprehension. Past reviews of visual world studies on perspective taking have largely taken the diverging findings of the various studies at face value, and attributed these apparently different findings to differences in the extent to which the paradigms used by different labs afford collaborative interaction. Researchers are asking questions about perspective taking of an increasingly nuanced and sophisticated nature, a clear indicator of progress. But this research has the potential not only to improve our understanding of conversational perspective taking. Grappling with problems of data interpretation in such a complex domain has the unique potential to drive visual world researchers to a deeper understanding of how to best map visual world data onto psycholinguistic theory. I will argue against this interactional affordances explanation, on two counts. First, it implies that interactivity affects the overall ability to form common ground, and thus provides no straightforward explanation of why, within a single noninteractive study, common ground can have very large effects on some aspects of processing (referential anticipation) while having negligible effects on others (lexical processing). Second, and more importantly, the explanation accepts the divergence in published findings at face value. However, a closer look at several key studies shows that the divergences are more likely to reflect inconsistent practices of analysis and interpretation that have been applied to an underlying body of data that is, in fact, surprisingly consistent. The diverging interpretations, I will argue, are the result of differences in the handling of anticipatory baseline effects (ABEs) in the analysis of visual world data. ABEs arise in perspective-taking studies because listeners have earlier access to constraining information about who knows what than they have to referential speech, and thus can already show biases in visual attention even before the processing of any referential speech has begun. To be sure, these ABEs clearly indicate early access to common ground; however, access does not imply integration, since it is possible that this information is not used later to modulate the processing of incoming speech. Failing to account for these biases using statistical or experimental controls leads to over-optimistic assessments of listeners’ ability to integrate this information with incoming speech. I will show that several key studies with varying degrees of interactional affordances all show similar temporal profiles of common ground use during the interpretive process: early anticipatory effects, followed by bottom-up effects of lexical processing that are not modulated by common ground, followed (optionally) by further late effects that are likely to be post-lexical. Furthermore, this temporal profile for common ground radically differs from the profile of contextual effects related to verb semantics. Together, these findings are consistent with the proposal that lexical processes are encapsulated from common ground, but cannot be straightforwardly accounted for by probabilistic constraint-based approaches