In this paper, we propose a novel method, retrieval, masking, and generation, for feedback comment generation. Feedback comment generation is a task in which a system generates feedback comments such as hints or explanatory notes for language learners, given input text and position showing where to comment. In the conventional study, the retrieve-and-edit method for retrieving feedback comments in the data pool and editing the comments has been thought effective for this task. However, the performance of this method does not perform as well as other conventional methods because its model learns to edit tokens that do not need to be rewritten in the retrieved comments. To mitigate this problem, we propose a method for combining retrieval, masking, and generation based on the retrieve-and-edit method. Specifically, tokens of feedback comments retrieved from the data pool are masked, and this masked feedback comment is used as a template to generate feedback comments. The proposed method should prevent unnecessary conversion by using not retrieved feedback comments directly but masking them. Our experiments on feedback comment generation demonstrate that the proposed method outperforms conventional methods.

This paper presents a novel multi-perspective document revision task. In conventional studies on document revision, tasks such as grammatical error correction, sentence reordering, and discourse relation classification have been performed individually; however, these tasks simultaneously should be revised to improve the readability and clarity of a whole document. Thus, our study defines multi-perspective document revision as a task that simultaneously revises multiple perspectives. To model the task, we design a novel Japanese multi-perspective document revision dataset that simultaneously handles seven perspectives to improve the readability and clarity of a document. Although a large amount of data that simultaneously handles multiple perspectives is needed to model multi-perspective document revision elaborately, it is difficult to prepare such a large amount of this data. Therefore, our study offers a multi-perspective document revision modeling method that can use a limited amount of matched data (i.e., data for the multi-perspective document revision task) and external partially-matched data (e.g., data for the grammatical error correction task). Experiments using our created dataset demonstrate the effectiveness of using multiple partially-matched datasets to model the multi-perspective document revision task.

With the increase of automatic speech recognition (ASR) applications, spoken-to-written style conversion that transforms spoken-style text into written-style text is becoming an important technology to increase the readability of ASR transcriptions. To establish such conversion technology, a parallel corpus of spoken-style text and written-style text is beneficial because it can be utilized for building end-to-end neural sequence transformation models. Spoken-to-written style conversion involves multiple conversion problems including punctuation restoration, disfluency detection, and simplification. However, most existing corpora tend to be made for just one of these conversion problems. In addition, in Japanese, we have to consider not only general spoken-to-written style conversion problems but also Japanese-specific ones, such as language style unification (e.g., polite, frank, and direct styles) and omitted postpositional particle expressions restoration. Therefore, we created a new Japanese parallel corpus of spoken-style text and written-style text that can simultaneously handle general problems and Japanese-specific ones. To make this corpus, we prepared four types of spoken-style text and utilized a crowdsourcing service for manually converting them into written-style text. This paper describes the building setup of this corpus and reports the baseline results of spoken-to-written style conversion using the latest neural sequence transformation models.

This paper presents a novel fusion method for integrating an external language model (LM) into the Transformer based sequence-to-sequence (seq2seq) model. While paired data are basically required to train the seq2seq model, the external LM can be trained with only unpaired data. Thus, it is important to leverage memorized knowledge in the external LM for building the seq2seq model, since it is hard to prepare a large amount of paired data. However, the existing fusion methods assume that the LM is integrated with recurrent neural network-based seq2seq models instead of the Transformer. Therefore, this paper proposes a fusion method that can explicitly utilize network structures in the Transformer. The proposed method, called memory attentive fusion, leverages the Transformer-style attention mechanism that repeats source-target attention in a multi-hop manner for reading the memorized knowledge in the LM. Our experiments on two text-style conversion tasks demonstrate that the proposed method performs better than conventional fusion methods.