Toshihiro Takahashi

Toshihiro Takahashi, Takaaki Tateishi, Michiaki Tatsubori

Semantic text similarity plays an important role in software engineering tasks in which engineers are requested to clarify the semantics of descriptive labels (e.g., business terms, table column names) that are often consists of too short or too generic words and appears in their IT systems. We formulate this type of problem as a task of matching descriptive labels to glossary descriptions. We then propose a framework to leverage an existing semantic text similarity measurement (STS) and augment it using semantic label enrichment and set-based collective contextualization where the former is a method to retrieve sentences relevant to a given label and the latter is a method to compute similarity between two contexts each of which is derived from a set of texts (e.g., column names in the same table). We performed an experiment on two datasets derived from publicly available data sources. The result indicated that the proposed methods helped the underlying STS correctly match more descriptive labels with the descriptions.

Takayuki Osogami, Toshihiro Takahashi

Autonomous agents need to make decisions in a sequential manner, under partially observable environment, and in consideration of how other agents behave. In critical situations, such decisions need to be made in real time for example to avoid collisions and recover to safe conditions. We propose a technique of tree search where a deterministic and pessimistic scenario is used after a specified depth. Because there is no branching with the deterministic scenario, the proposed technique allows us to take into account the events that can occur far ahead in the future. The effectiveness of the proposed technique is demonstrated in Pommerman, a multi-agent environment used in a NeurIPS 2018 competition, where the agents that implement the proposed technique have won the first and third places.