We have constructed the Corpus of Everyday Japanese Conversation (CEJC) and published it in March 2022. The CEJC is designed to contain various kinds of everyday conversations in a balanced manner to capture their diversity. The CEJC features not only audio but also video data to facilitate precise understanding of the mechanism of real-life social behavior. The publication of a large-scale corpus of everyday conversations that includes video data is a new approach. The CEJC contains 200 hours of speech, 577 conversations, about 2.4 million words, and a total of 1675 conversants. In this paper, we present an overview of the corpus, including the recording method and devices, structure of the corpus, formats of video and audio files, transcription, and annotations. We then report some results of the evaluation of the CEJC in terms of conversant and conversation attributes. We show that the CEJC includes a good balance of adult conversants in terms of gender and age, as well as a variety of conversations in terms of conversation forms, places, activities, and numbers of conversants.

The National Institute for Japanese Language and Linguistics, Japan (NINJAL, Japan), has developed several types of corpora. For each corpus NINJAL provided an online search environment, ‘Chunagon’, which is a morphological-information-annotation-based concordance system made publicly available in 2011. NINJAL has now provided a skewer-search system ‘Kotonoha’ based on the ‘Chunagon’ systems. This system enables querying of multiple corpora by certain categories, such as register type and period.

In 2016, we set about building a large-scale corpus of everyday Japanese conversation―a collection of conversations embedded in naturally occurring activities in daily life. We will collect more than 200 hours of recordings over six years,publishing the corpus in 2022. To construct such a huge corpus, we have conducted a pilot project, one of whose purposes is to establish a corpus design for collecting various kinds of everyday conversations in a balanced manner. For this purpose, we conducted a survey of everyday conversational behavior, with about 250 adults, in order to reveal how diverse our everyday conversational behavior is and to build an empirical foundation for corpus design. The questionnaire included when, where, how long,with whom, and in what kind of activity informants were engaged in conversations. We found that ordinary conversations show the following tendencies: i) they mainly consist of chats, business talks, and consultations; ii) in general, the number of participants is small and the duration of the conversation is short; iii) many conversations are conducted in private places such as homes, as well as in public places such as offices and schools; and iv) some questionnaire items are related to each other. This paper describes an overview of this survey study, and then discusses how to design a large-scale corpus of everyday Japanese conversation on this basis.

In this paper, we describe the design and development of a new version of the Corpus of Spontaneous Japanese (CSJ), which is a large-scale spoken corpus released in 2004. CSJ contains various annotations that are represented in XML format (CSJ-XML). CSJ-XML, however, is very complicated and suffers from some problems. To overcome this problem, we have developed and released, in 2013, a relational database version of CSJ (CSJ-RDB). CSJ-RDB is based on an extension of the segment and link-based annotation scheme, which we adapted to handle multi-channel and multi-modal streams. Because this scheme adopts a stand-off framework, CSJ-RDB can represent three hierarchical structures at the same time: inter-pausal-unit-top, clause-top, and intonational-phrase-top. CSJ-RDB consists of five different types of tables: segment, unaligned-segment, link, relation, and meta-information tables. The database was automatically constructed from annotation files extracted from CSJ-XML by using general-purpose corpus construction tools. CSJ-RDB enables us to easily and efficiently conduct complex searches required for corpus-based studies of spoken language.

Because of the tremendous effort required for recording and transcription, large-scale spoken language corpora have been hardly developed in Japanese, with a notable exception of the Corpus of Spontaneous Japanese (CSJ). Various research groups have individually developed conversation corpora in Japanese, but these corpora are transcribed by different conventions and have few annotations in common, and some of them lack fundamental annotations, which are prerequisites for conversation research. To solve this situation by sharing existing conversation corpora that cover diverse styles and settings, we have tried to automatically transform a transcription made by one convention into that made by another convention. Using a conversation corpus transcribed in both the Conversation-Analysis-style (CA-style) and CSJ-style, we analyzed the correspondence between CA’s ‘intonation markers’ and CSJ’s ‘tone labels,’ and constructed a statistical model that converts tone labels into intonation markers with reference to linguistic and acoustic features of the speech. The result showed that there is considerable variance in intonation marking even between trained transcribers. The model predicted with 85% accuracy the presence of the intonation markers, and classified the types of the markers with 72% accuracy.

In this paper, we propose a new scheme for annotating response tokens (RTs) and their triggering expressions in Japanese multi-party conversations. In the proposed scheme, RTs are first identified and classified according to their forms, and then sub-classified according to their sequential positions in the discourse. To deeply study the contexts in which RTs are used, the scheme also provides procedures for annotating triggering expressions, which are considered to trigger the listener's production of RTs. RTs are classified according to whether or not there is a particular object or proposition in the speaker's turn for which the listener shows a positive or aligned stance. Triggering expressions are then identified in the speaker's turn; they include surprising facts and other newsworthy things, opinions and assessments, focus of a response to a question or repair initiation, keywords in narratives, and embedded propositions quoted from other's statement or thought, which are to be agreed upon, assessed, or noticed. As an illustrative application of our scheme, we present a preliminary analysis on the distribution of the latency of the listener's response to the triggering expression, showing how it differs according to RT's forms and positions.

Compilation of a 100 million words balanced corpus called the Balanced Corpus of Contemporary Written Japanese (or BCCWJ) is underway at the National Institute for Japanese Language and Linguistics. The corpus covers a wide range of text genres including books, magazines, newspapers, governmental white papers, textbooks, minutes of the National Diet, internet text (bulletin board and blogs) and so forth, and when possible, samples are drawn from the rigidly defined statistical populations by means of random sampling. All texts are dually POS-analyzed based upon two different, but mutually related, definitions of ‘word.’ Currently, more than 90 million words have been sampled and XML annotated with respect to text-structure and lexical and character information. A preliminary linear discriminant analysis of text genres using the data of POS frequencies and sentence length revealed it was possible to classify the text genres with a correct identification rate of 88% as far as the samples of books, newspapers, whitepapers, and internet bulletin boards are concerned. When the samples of blogs were included in this data set, however, the identification rate went down to 68%, suggesting the considerable variance of the blog texts in terms of the textual register and style.

In this paper, we propose a scheme for annotating utterance-level units in Japanese dialogs, which emerged from an analysis of the interrelationship among four schemes, i) inter-pausal units, ii) intonation units, iii) clause units, and iv) pragmatic units. The associations among the labels of these four units were illustrated by multiple correspondence analysis and hierarchical cluster analysis. Based on these results, we prescribe utterance-unit identification rules, which identify two sorts of utterance-units with different granularities: short and long utterance-units. Short utterance-units are identified by acoustic and prosodic disjuncture, and they are considered to constitute units of speaker's planning and hearer's understanding. Long utterance-units, on the other hand, are recognized by syntactic and pragmatic disjuncture, and they are regarded as units of interaction. We explore some characteristics of these utterance-units, focusing particularly on unit duration and syntactic property, other participants' responses, and mismatch between the two-levels. We also discuss how our two-level utterance-units are useful in analyzing cognitive and communicative aspects of spoken dialogs.