SLAM-Inspired Simultaneous Contextualization and Interpreting for Incremental Conversation Sentences

Distributed representation of words has improved the performance for many natural language tasks. In many methods, however, only one meaning is considered for one label of a word, and multiple meanings of polysemous words depending on the context are rarely handled. Although research works have dealt with polysemous words, they determine the meanings of such words according to a batch of large documents. Hence, there are two problems with applying these methods to sequential sentences, as in a conversation that contains ambiguous expressions. The first problem is that the methods cannot sequentially deal with the interdependence between context and word interpretation, in which context is decided by word interpretations and the word interpretations are decided by the context. Context estimation must thus be performed in parallel to pursue multiple interpretations. The second problem is that the previous methods use large-scale sets of sentences for offline learning of new interpretations, and the steps of learning and inference are clearly separated. Such methods using offline learning cannot obtain new interpretations during a conversation. Hence, to dynamically estimate the conversation context and interpretations of polysemous words in sequential sentences, we propose a method of Simultaneous Contextualization And INterpreting (SCAIN) based on the traditional Simultaneous Localization And Mapping (SLAM) algorithm. By using the SCAIN algorithm, we can sequentially optimize the interdependence between context and word interpretation while obtaining new interpretations online. For experimental evaluation, we created two datasets: one from Wikipedia's disambiguation pages and the other from real conversations. For both datasets, the results confirmed that SCAIN could effectively achieve sequential optimization of the interdependence and acquisition of new interpretations.