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Distributed representation of words has improved the performance for manynatural language tasks. In many methods, however, only one meaning isconsidered for one label of a word, and multiple meanings of polysemous wordsdepending on the context are rarely handled. Although research works have dealtwith polysemous words, they determine the meanings of such words according to abatch of large documents. Hence, there are two problems with applying thesemethods to sequential sentences, as in a conversation that contains ambiguousexpressions. The first problem is that the methods cannot sequentially dealwith the interdependence between context and word interpretation, in whichcontext is decided by word interpretations and the word interpretations aredecided by the context. Context estimation must thus be performed in parallelto pursue multiple interpretations. The second problem is that the previousmethods use large-scale sets of sentences for offline learning of newinterpretations, and the steps of learning and inference are clearly separated.Such methods using offline learning cannot obtain new interpretations during aconversation. Hence, to dynamically estimate the conversation context andinterpretations of polysemous words in sequential sentences, we propose amethod of Simultaneous Contextualization And INterpreting (SCAIN) based on thetraditional Simultaneous Localization And Mapping (SLAM) algorithm. By usingthe SCAIN algorithm, we can sequentially optimize the interdependence betweencontext and word interpretation while obtaining new interpretations online. Forexperimental evaluation, we created two datasets: one from Wikipedia'sdisambiguation pages and the other from real conversations. For both datasets,the results confirmed that SCAIN could effectively achieve sequentialoptimization of the interdependence and acquisition of new interpretations.

SLAM-Inspired Simultaneous Contextualization and Interpreting for Incremental Conversation Sentences