Jin’ichi Murakami

Pattern dictionary development based on non-compositional language model for japanese compound and complex sentences

A large-scale sentence pattern dictionary (SP-dictionary) for Japanese compound and complex sentences has been developed. The dictionary has been compiled based on the non-compositional language model. Sentences with 2 or 3 predicates are extracted from a Japanese-to-English parallel corpus of 1 million sentences, and the compositional constituents contained within them are generalized to produce a SP-dictionary containing a total of 215,000 pattern pairs. In evaluation tests, the SP-dictionary achieved a syntactic coverage of 92% and a semantic coverage of 70%.

Construction and evaluation of text-dialog corpus with emotion tags focusing on facial expression in comics

Large-scale text-dialog corpora with emotion tags are required to generate a knowledge base for emotional reasoning from text. Annotating emotion tags is known to suffer from problems with instability. These are caused by the lack of non-linguistic expressions (e.g. speech and facial expressions) in the text dialog. We aimed to construct a stable, usable text-dialog corpus with emotion tags. We first focused on facial expression in comics. Some comics contain many text dialogs that are similar to everyday conversation, and it is worth analyzing their text. We therefore extracted 29,538 sentences from 10 comic books and annotated face tags and emotion tags. Two annotators independently placed “temporary face/emotion tags” on stories and then decided what the “correct face/emotion tags” were by discussing them with each other. They acquired 16,635 correct emotion tags as a result. We evaluated the stability and usability of the corpus. We evaluated the correspondence between temporary and correct tags to assess stability, and found precision was 83.8% and recall was 78.8%. These were higher than for annotation without facial expressions (precision = 56.2%, recall = 51.5%). We extracted emotional suffix expressions from the corpus using a probabilistic method to evaluate usability. We could thus construct a text-dialog corpus with emotion tags and confirm its stability and usability.

Machine Translation Method Based on Non-compositional Semantics (Word-Level Sentence-Pattern-Based MT)

To overcome the conventional machine translation method, Ikehara et al. proposed a machine translation scheme based on non-compositional semantics. This machine translation scheme requires many sentence patterns which can preserve the semantics of the expression structure. To use this machine translation scheme for Japanese-English machine translation, a compound and complex sentence pattern dictionary, called “ToribankSPD”, have been developed. This dictionary has three levels of sentence patterns: “word-level”, “phrase-level”, and “clause-level”. In this paper, according to the machine translation scheme based on non-compositional semantics, we implemented the Japanese-English sentence-pattern-based machine translation method using the word-level sentence patterns of ToribankSPD. In our experiments, the pattern matching rate was low (about 10 %). However, 72 out of 100 evaluated sentences used the sentence patterns that had an appropriate expression structure, and the translation accuracy of 55 sentences was high.

Non-Compositional Language Model and Pattern Dictionary Development for Japanese Compound and Complex Sentences

To realize high quality machine translation, we proposed a Non-Compositional Language Model, and developed a sentence pattern dictionary of 226,800 pattern pairs for Japanese compound and complex sentences consisting of 2 or 3 clauses. In pattern generation from a parallel corpus, Compositional Constituents that could be generalized were 74% of independent words, 24% of phrases and only 15% of clauses. This means that in Japanese-to-English MT, most of the translation results as shown in the parallel corpus could not be obtained by methods based on Compositional Semantics. This dictionary achieved a syntactic coverage of 98% and a semantic coverage of 78%. It will substantially improve translation quality.

Phrase-Level Pattern-Based Machine Translation Based on Analogical Mapping Method

To overcome the conventional method based on Compositional Semantics, the Analogical Mapping Method was developed. Implementing this method requires a translation method based on sentence patterns. Although a word-level pattern-based translation system already exists in Japanese-English machine translation, this paper describes the new phrase-level pattern-based translation system. The results of translation experiments show that the quality of phrase translation is still low. However, these problems are to be resolved in our future work.