Neural text generation models that are conditioned on a given input (e.g., machine translation and image captioning) are typically trained through maximum likelihood estimation of the target text. However, models trained in this manner often suffer from various types of errors when making subsequent inferences. In this study, we propose suppressing an arbitrary type of error by training the text generation model in a reinforcement learning framework; herein, we use a trainable reward function that can discriminate between references and sentences, containing the targeted type of errors. We create such negative examples by artificially injecting the targeted errors into the references. In the experiments, we focus on two error types; repeated and dropped tokens in model-generated text. The experimental results demonstrate that our method can suppress generation errors, and achieves significant improvements on two machine translation and two image captioning tasks.

Neural machine translation often suffers from an under-translation problem owing to its limited modeling of the output sequence lengths. In this study, we propose a novel approach to training a Transformer model using length constraints based on length-aware positional encoding (PE). Because length constraints with exact target sentence lengths degrade the translation performance, we add a random perturbation with a uniform distribution within a certain range to the length constraints in the PE during the training. In the inference step, we predicted the output lengths from the input sequences using a length prediction model based on a large-scale pre-trained language model. In Japanese-to-English and English-to-Japanese translation, experimental results show that the proposed perturbation injection improves the robustness of the length prediction errors, particularly within a certain range.

Okazaki et al. (2018) have proposed a method for organizing the information contained in multiple documents into a table without limiting the information to be extracted. In this study, we propose a method for improving the accuracy of these tables. In our proposed method, information is first clustered hierarchically. Next, for the results of hierarchical clustering (with the number of clusters ranging from 1 to n), the degree of filling and the information density of the resulting table are calculated. The number of clusters when the balance between these two indicators is optimal is chosen as the optimal number of clusters. The results of the method using the chosen number of clusters are organized into a table. In the conventional method, the number of clusters estimated by the X-means method tends to be too small. As demonstrated by the results of experiments using 15 types of multiple documents, the proposed method improves this problem, with its estimated number of clusters being closer to the optimum. The average evaluation result in the tables (F-measure) when applying the conventional method was 0.43; the proposed method improves this to 0.65. We therefore confirm the effectiveness of the proposed method.

本稿では，アイヌ民話（ウウェペケㇾ）の音声認識に関する我々の取り組みについて述べる．まず，2 つの博物館から提供されたアイヌ語アーカイブのデータを元に，沙流方言を対象としたアイヌ語音声コーパスを構築した．次に，このコーパスを用いて注意機構モデルに基づく音声認識システムを構成し，音素・音節・ワードピース・単語の 4 つの認識単位について検討した．その結果，音節単位での音声認識精度が最も高くなることがわかり，話者クローズド条件と話者オープン条件のそれぞれについて，音素認識精度で 93.7% と 86.2%，単語認識精度で 78.3% と 61.4% を実現した．音声認識精度が話者オープン条件において大幅に低下する問題に対して，CycleGAN を用いた教師なし話者適応を提案した．これは，学習データ内の話者の音声から認識対象話者の音声への写像を CycleGAN に学習させ，学習データ内の音声を全て認識対象話者風の音声に変換するものである．本手法によって最大で相対 60.6% の音素誤り率の改善を得た．さらに，日本語とアイヌ語が混合した音声における言語識別についても検討を行い，音素認識と単語認識を用いた構成で一定の識別性能を達成できることを示した．

近年，シンボルグラウンディングや言語生成，自然言語による非言語データの検索など，実世界に紐づいた自然言語処理への注目が高まっている．我々は，将棋のゲーム局面に付随する解説文がこれらの課題の興味深いテストベッドになると考えている．解説者は現在の局面だけでなく過去や未来の指し手に言及しており，これらはゲーム木にグラウンディングされることから，ゲーム木探索アルゴリズムを活用した実世界対応の研究が期待できる．本論文では，我々が構築した，人手による単語分割・固有表現・モダリティ表現・事象の事実性のアノテーションを行った将棋解説文コーパスを説明する．