The task of generating weather-forecast comments from meteorological simulations has the following requirements: (i) the changes in numerical values for various physical quantities need to be considered, (ii) the weather comments should be dependent on delivery time and area information, and (iii) the comments should provide useful information for users. To meet these requirements, we propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. We also introduce weather labels representing weather information, such as sunny and rain, for our model to explicitly describe useful information. We conducted automatic and human evaluations. The results indicate that our model performed best against baselines in terms of informativeness. We make our code and data publicly available.

Numerical tables are widely used to present experimental results in scientific papers. For table understanding, a metric-type is essential to discriminate numbers in the tables. We introduce a new information extraction task, metric-type identification from multi-level header numerical tables, and provide a dataset extracted from scientific papers consisting of header tables, captions, and metric-types. We then propose two joint-learning neural classification and generation schemes featuring pointer-generator-based and BERT-based models. Our results show that the joint models can handle both in-header and out-of-header metric-type identification problems.

This work presents multi-modal deep SVDD (mSVDD) for one-class text classification. By extending the uni-modal SVDD to a multiple modal one, we build mSVDD with multiple hyperspheres, that enable us to build a much better description for target one-class data. Additionally, the end-to-end architecture of mSVDD can jointly handle neural feature learning and one-class text learning. We also introduce a mechanism for incorporating negative supervision in the absence of real negative data, which can be beneficial to the mSVDD model. We conduct experiments on Reuters and 20 Newsgroup datasets, and the experimental results demonstrate that mSVDD outperforms uni-modal SVDD and mSVDD can get further improvements when negative supervision is incorporated.

Character-aware neural language models can capture the relationship between words by exploiting character-level information and are particularly effective for languages with rich morphology. However, these models are usually biased towards information from surface forms. To alleviate this problem, we propose a simple and effective method to improve a character-aware neural language model by forcing a character encoder to produce word-based embeddings under Skip-gram architecture in a warm-up step without extra training data. We empirically show that the resulting character-aware neural language model achieves obvious improvements of perplexity scores on typologically diverse languages, that contain many low-frequency or unseen words.

In the social media, users frequently use small images called emojis in their posts. Although using emojis in texts plays a key role in recent communication systems, less attention has been paid on their positions in the given texts, despite that users carefully choose and put an emoji that matches their post. Exploring positions of emojis in texts will enhance understanding of the relationship between emojis and texts. We extend an emoji label prediction task taking into account the information of emoji positions, by jointly learning the emoji position in a tweet to predict the emoji label. The results demonstrate that the position of emojis in texts is a good clue to boost the performance of emoji label prediction. Human evaluation validates that there exists a suitable emoji position in a tweet, and our proposed task is able to make tweets more fancy and natural. In addition, considering emoji position can further improve the performance for the irony detection task compared to the emoji label prediction. We also report the experimental results for the modified dataset, due to the problem of the original dataset for the first shared task to predict an emoji label in SemEval2018.

Neural sequence-to-sequence (Seq2Seq) models and BERT have achieved substantial improvements in abstractive document summarization (ADS) without and with pre-training, respectively. However, they sometimes repeatedly attend to unimportant source phrases while mistakenly ignore important ones. We present reconstruction mechanisms on two levels to alleviate this issue. The sequence-level reconstructor reconstructs the whole document from the hidden layer of the target summary, while the word embedding-level one rebuilds the average of word embeddings of the source at the target side to guarantee that as much critical information is included in the summary as possible. Based on the assumption that inverse document frequency (IDF) measures how important a word is, we further leverage the IDF weights in our embedding-level reconstructor. The proposed frameworks lead to promising improvements for ROUGE metrics and human rating on both the CNN/Daily Mail and Newsroom summarization datasets.

Encoder-decoder models have been commonly used for many tasks such as machine translation and response generation. As previous research reported, these models suffer from generating redundant repetition. In this research, we propose a new mechanism for encoder-decoder models that estimates the semantic difference of a source sentence before and after being fed into the encoder-decoder model to capture the consistency between two sides. This mechanism helps reduce repeatedly generated tokens for a variety of tasks. Evaluation results on publicly available machine translation and response generation datasets demonstrate the effectiveness of our proposal.

Generating texts in scientific papers requires not only capturing the content contained within the given input but also frequently acquiring the external information called context. We push forward the scientific text generation by proposing a new task, namely context-aware text generation in the scientific domain, aiming at exploiting the contributions of context in generated texts. To this end, we present a novel challenging large-scale Scientific Paper Dataset for ConteXt-Aware Text Generation (SciXGen), consisting of well-annotated 205,304 papers with full references to widely-used objects (e.g., tables, figures, algorithms) in a paper. We comprehensively benchmark, using state-of-the-arts, the efficacy of our newly constructed SciXGen dataset in generating description and paragraph. Our dataset and benchmarks will be made publicly available to hopefully facilitate the scientific text generation research.

Although there are many studies on neural language generation (NLG), few trials are put into the real world, especially in the advertising domain. Generating ads with NLG models can help copywriters in their creation. However, few studies have adequately evaluated the effect of generated ads with actual serving included because it requires a large amount of training data and a particular environment. In this paper, we demonstrate a practical use case of generating ad-text with an NLG model. Specially, we show how to improve the ads’ impact, deploy models to a product, and evaluate the generated ads.

Recent neural text generation models have shown significant improvement in generating descriptive text from structured data such as table formats. One of the remaining important challenges is generating more analytical descriptions that can be inferred from facts in a data source. The use of a template-based generator and a pointer-generator is among the potential alternatives for table-to-text generators. In this paper, we propose a framework consisting of a pre-trained model and a copy mechanism. The pre-trained models are fine-tuned to produce fluent text that is enriched with numerical reasoning. However, it still lacks fidelity to the table contents. The copy mechanism is incorporated in the fine-tuning step by using general placeholders to avoid producing hallucinated phrases that are not supported by a table while preserving high fluency. In summary, our contributions are (1) a new dataset for numerical table-to-text generation using pairs of a table and a paragraph of a table description with richer inference from scientific papers, and (2) a table-to-text generation framework enriched with numerical reasoning.

We propose the task of automatically generating commentaries for races in a motor racing game, from vision, structured numerical, and textual data. Commentaries provide information to support spectators in understanding events in races. Commentary generation models need to interpret the race situation and generate the correct content at the right moment. We divide the task into two subtasks: utterance timing identification and utterance generation. Because existing datasets do not have such alignments of data in multiple modalities, this setting has not been explored in depth. In this study, we introduce a new large-scale dataset that contains aligned video data, structured numerical data, and transcribed commentaries that consist of 129,226 utterances in 1,389 races in a game. Our analysis reveals that the characteristics of commentaries change over time or from viewpoints. Our experiments on the subtasks show that it is still challenging for a state-of-the-art vision encoder to capture useful information from videos to generate accurate commentaries. We make the dataset and baseline implementation publicly available for further research.

Story generation is a task that aims to automatically generate a meaningful story. This task is challenging because it requires high-level understanding of the semantic meaning of sentences and causality of story events. Naivesequence-to-sequence models generally fail to acquire such knowledge, as it is difficult to guarantee logical correctness in a text generation model without strategic planning. In this study, we focus on planning a sequence of events assisted by event graphs and use the events to guide the generator. Rather than using a sequence-to-sequence model to output a sequence, as in some existing works, we propose to generate an event sequence by walking on an event graph. The event graphs are built automatically based on the corpus. To evaluate the proposed approach, we incorporate human participation, both in event planning and story generation. Based on the largescale human annotation results, our proposed approach has been shown to provide more logically correct event sequences and stories compared with previous approaches.

We propose a simple and effective method for incorporating word clusters into the Continuous Bag-of-Words (CBOW) model. Specifically, we propose to replace infrequent input and output words in CBOW model with their clusters. The resulting cluster-incorporated CBOW model produces embeddings of frequent words and a small amount of cluster embeddings, which will be fine-tuned in downstream tasks. We empirically show our replacing method works well on several downstream tasks. Through our analysis, we show that our method might be also useful for other similar models which produce word embeddings.

We report our system description for the RDFto-Text task in English on the WebNLG 2020 Challenge. Our approach consists of two parts: 1) RDF-to-Text Generation Pipeline and 2) Plan Selection. RDF-to-Text Generation Pipeline is built on the state-of-the-art pretraining model, while Plan Selection helps decide the proper plan into the pipeline.

End-to-end models on data-to-text learn the mapping of data and text from the aligned pairs in the dataset. However, these alignments are not always obtained reliably, especially for the time-series data, for which real time comments are given to some situation and there might be a delay in the comment delivery time compared to the actual event time. To handle this issue of possible noisy alignments in the dataset, we propose a neural network model with multi-timestep data and a copy mechanism, which allows the models to learn the correspondences between data and text from the dataset with noisier alignments. We focus on generating market comments in Japanese that are delivered each time an event occurs in the market. The core idea of our approach is to utilize multi-timestep data, which is not only the latest market price data when the comment is delivered, but also the data obtained at several timesteps earlier. On top of this, we employ a copy mechanism that is suitable for referring to the content of data records in the market price data. We confirm the superiority of our proposal by two evaluation metrics and show the accuracy improvement of the sentence generation using the time series data by our proposed method.

We present a neural exhaustive approach that addresses named entity recognition (NER) and relation recognition (RE), for the entity and re- lation recognition over the wet-lab protocols shared task. We introduce BERT-based neural exhaustive approach that enumerates all pos- sible spans as potential entity mentions and classifies them into entity types or no entity with deep neural networks to address NER. To solve relation extraction task, based on the NER predictions or given gold mentions we create all possible trigger-argument pairs and classify them into relation types or no relation. In NER task, we achieved 76.60% in terms of F-score as third rank system among the partic- ipated systems. In relation extraction task, we achieved 80.46% in terms of F-score as the top system in the relation extraction or recognition task. Besides we compare our model based on the wet lab protocols corpus (WLPC) with the WLPC baseline and dynamic graph-based in- formation extraction (DyGIE) systems.

We tackle the task of automatically generating a function name from source code. Existing generators face difficulties in generating low-frequency or out-of-vocabulary subwords. In this paper, we propose two strategies for copying low-frequency or out-of-vocabulary subwords in inputs. Our best performing model showed an improvement over the conventional method in terms of our modified F1 and accuracy on the Java-small and Java-large datasets.

We propose neural models that can normalize text by considering the similarities of word strings and sounds. We experimentally compared a model that considers the similarities of both word strings and sounds, a model that considers only the similarity of word strings or of sounds, and a model without the similarities as a baseline. Results showed that leveraging the word string similarity succeeded in dealing with misspellings and abbreviations, and taking into account the sound similarity succeeded in dealing with phonetic substitutions and emphasized characters. So that the proposed models achieved higher F1 scores than the baseline.

Existing models for data-to-text tasks generate fluent but sometimes incorrect sentences e.g., “Nikkei gains” is generated when “Nikkei drops” is expected. We investigate models trained on contrastive examples i.e., incorrect sentences or terms, in addition to correct ones to reduce such errors. We first create rules to produce contrastive examples from correct ones by replacing frequent crucial terms such as “gain” or “drop”. We then use learning methods with several losses that exploit contrastive examples. Experiments on the market comment generation task show that 1) exploiting contrastive examples improves the capability of generating sentences with better lexical choice, without degrading the fluency, 2) the choice of the loss function is an important factor because the performances on different metrics depend on the types of loss functions, and 3) the use of the examples produced by some specific rules further improves performance. Human evaluation also supports the effectiveness of using contrastive examples.

In this paper, we evaluate the progress of our field toward solving simple factoid questions over a knowledge base, a practically important problem in natural language interface to database. As in other natural language understanding tasks, a common practice for this task is to train and evaluate a model on a single dataset, and recent studies suggest that SimpleQuestions, the most popular and largest dataset, is nearly solved under this setting. However, this common setting does not evaluate the robustness of the systems outside of the distribution of the used training data. We rigorously evaluate such robustness of existing systems using different datasets. Our analysis, including shifting of training and test datasets and training on a union of the datasets, suggests that our progress in solving SimpleQuestions dataset does not indicate the success of more general simple question answering. We discuss a possible future direction toward this goal.

Coreference resolution is the task of identifying all mentions in a text that refer to the same real-world entity. Collecting sufficient labelled data from expert annotators to train a high-performance coreference resolution system is time-consuming and expensive. Crowdsourcing makes it possible to obtain the required amounts of data rapidly and cost-effectively. However, crowd-sourced labels can be noisy. To ensure high-quality data, it is crucial to infer the correct labels by aggregating the noisy labels. In this paper, we split the aggregation into two subtasks, i.e, mention classification and coreference chain inference. Firstly, we predict the general class of each mention using an autoencoder, which incorporates contextual information about each mention, while at the same time taking into account the mention’s annotation complexity and annotators’ reliability at different levels. Secondly, to determine the coreference chain of each mention, we use weighted voting which takes into account the learned reliability in the first subtask. Experimental results demonstrate the effectiveness of our method in predicting the correct labels. We also illustrate our model’s interpretability through a comprehensive analysis of experimental results.

We explore the utilities of explicit negative examples in training neural language models. Negative examples here are incorrect words in a sentence, such as barks in *The dogs barks. Neural language models are commonly trained only on positive examples, a set of sentences in the training data, but recent studies suggest that the models trained in this way are not capable of robustly handling complex syntactic constructions, such as long-distance agreement. In this paper, we first demonstrate that appropriately using negative examples about particular constructions (e.g., subject-verb agreement) will boost the model’s robustness on them in English, with a negligible loss of perplexity. The key to our success is an additional margin loss between the log-likelihoods of a correct word and an incorrect word. We then provide a detailed analysis of the trained models. One of our findings is the difficulty of object-relative clauses for RNNs. We find that even with our direct learning signals the models still suffer from resolving agreement across an object-relative clause. Augmentation of training sentences involving the constructions somewhat helps, but the accuracy still does not reach the level of subject-relative clauses. Although not directly cognitively appealing, our method can be a tool to analyze the true architectural limitation of neural models on challenging linguistic constructions.

We propose a global optimization method under length constraint (GOLC) for neural text summarization models. GOLC increases the probabilities of generating summaries that have high evaluation scores, ROUGE in this paper, within a desired length. We compared GOLC with two optimization methods, a maximum log-likelihood and a minimum risk training, on CNN/Daily Mail and a Japanese single document summarization data set of The Mainichi Shimbun Newspapers. The experimental results show that a state-of-the-art neural summarization model optimized with GOLC generates fewer overlength summaries while maintaining the fastest processing speed; only 6.70% overlength summaries on CNN/Daily and 7.8% on long summary of Mainichi, compared to the approximately 20% to 50% on CNN/Daily Mail and 10% to 30% on Mainichi with the other optimization methods. We also demonstrate the importance of the generation of in-length summaries for post-editing with the dataset Mainich that is created with strict length constraints. The ex- perimental results show approximately 30% to 40% improved post-editing time by use of in-length summaries.

We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking module selects and keeps track of salient information and memorizes which record has been mentioned. Our generation module generates a summary conditioned on the state of tracking module. Our proposed model is considered to simulate the human-like writing process that gradually selects the information by determining the intermediate variables while writing the summary. In addition, we also explore the effectiveness of the writer information for generations. Experimental results show that our proposed model outperforms existing models in all evaluation metrics even without writer information. Incorporating writer information further improves the performance, contributing to content planning and surface realization.

In this paper, we attempt to answer the question of whether neural network models can learn numeracy, which is the ability to predict the magnitude of a numeral at some specific position in a text description. A large benchmark dataset, called Numeracy-600K, is provided for the novel task. We explore several neural network models including CNN, GRU, BiGRU, CRNN, CNN-capsule, GRU-capsule, and BiGRU-capsule in the experiments. The results show that the BiGRU model gets the best micro-averaged F1 score of 80.16%, and the GRU-capsule model gets the best macro-averaged F1 score of 64.71%. Besides discussing the challenges through comprehensive experiments, we also present an important application scenario, i.e., detecting exaggerated information, for the task.

We propose a data-to-document generator that can easily control the contents of output texts based on a neural language model. Conventional data-to-text model is useful when a reader seeks a global summary of data because it has only to describe an important part that has been extracted beforehand. However, because depending on users, it differs what they are interested in, so it is necessary to develop a method to generate various summaries according to users’ interests. We develop a model to generate various summaries and to control their contents by providing the explicit targets for a reference to the model as controllable factors. In the experiments, we used five-minute or one-hour charts of 9 indicators (e.g., Nikkei225), as time-series data, and daily summaries of Nikkei Quick News as textual data. We conducted comparative experiments using two pieces of information: human-designed topic labels indicating the contents of a sentence and automatically extracted keywords as the referential information for generation.

We propose a simple and effective method to inject word-level information into character-aware neural language models. Unlike previous approaches which usually inject word-level information at the input of a long short-term memory (LSTM) network, we inject it into the softmax function. The resultant model can be seen as a combination of character-aware language model and simple word-level language model. Our injection method can also be used together with previous methods. Through the experiments on 14 typologically diverse languages, we empirically show that our injection method, when used together with the previous methods, works better than the previous methods, including a gating mechanism, averaging, and concatenation of word vectors. We also provide a comprehensive comparison of these injection methods.

Discourse relations between sentences are often represented as a tree, and the tree structure provides important information for summarizers to create a short and coherent summary. However, current neural network-based summarizers treat the source document as just a sequence of sentences and ignore the tree-like discourse structure inherent in the document. To incorporate the information of a discourse tree structure into the neural network-based summarizers, we propose a discourse-aware neural extractive summarizer which can explicitly take into account the discourse dependency tree structure of the source document. Our discourse-aware summarizer can jointly learn the discourse structure and the salience score of a sentence by using novel hierarchical attention modules, which can be trained on automatically parsed discourse dependency trees. Experimental results showed that our model achieved competitive or better performances against state-of-the-art models in terms of ROUGE scores on the DailyMail dataset. We further conducted manual evaluations. The results showed that our approach also gained the coherence of the output summaries.

We present a neural pipeline approach that performs named entity recognition (NER) and concept indexing (CI), which links them to concept unique identifiers (CUIs) in a knowledge base, for the PharmaCoNER shared task on pharmaceutical drugs and chemical entities. We proposed a neural NER model that captures the surrounding semantic information of a given sequence by capturing the forward- and backward-context of bidirectional LSTM (Bi-LSTM) output of a target span using contextual span representation-based exhaustive approach. The NER model enumerates all possible spans as potential entity mentions and classify them into entity types or no entity with deep neural networks. For representing span, we compare several different neural network architectures and their ensembling for the NER model. We then perform dictionary matching for CI and, if there is no matching, we further compute similarity scores between a mention and CUIs using entity embeddings to assign the CUI with the highest score to the mention. We evaluate our approach on the two sub-tasks in the shared task. Among the five submitted runs, the best run for each sub-task achieved the F-score of 86.76% on Sub-task 1 (NER) and the F-score of 79.97% (strict) on Sub-task 2 (CI).

This paper describes our system developed for the coreference resolution task of the CRAFT Shared Tasks 2019. The CRAFT corpus is more challenging than other existing corpora because it contains full text articles. We have employed an existing span-based state-of-theart neural coreference resolution system as a baseline system. We enhance the system with two different techniques to capture longdistance coreferent pairs. Firstly, we filter noisy mentions based on parse trees with increasing the number of antecedent candidates. Secondly, instead of relying on the LSTMs, we integrate the highly expressive language model–BERT into our model. Experimental results show that our proposed systems significantly outperform the baseline. The best performing system obtained F-scores of 44%, 48%, 39%, 49%, 40%, and 57% on the test set with B3, BLANC, CEAFE, CEAFM, LEA, and MUC metrics, respectively. Additionally, the proposed model is able to detect coreferent pairs in long distances, even with a distance of more than 200 sentences.

This paper explores the influence of spelling errors on lexical variation measures. Lexical richness measures such as Type-Token Ration (TTR) and Yule’s K are often used for learner English analysis and assessment. When applied to learner English, however, they can be unreliable because of the spelling errors appearing in it. Namely, they are, directly or indirectly, based on the counts of distinct word types, and spelling errors undesirably increase the number of distinct words. This paper introduces and examines the hypothesis that lexical richness measures become unstable in learner English because of spelling errors. Specifically, it tests the hypothesis on English learner corpora of three groups (middle school, high school, and college students). To be precise, it estimates the difference in TTR and Yule’s K caused by spelling errors, by calculating their values before and after spelling errors are manually corrected. Furthermore, it examines the results theoretically and empirically to deepen the understanding of the influence of spelling errors on them.

This study proposes a new neural machine translation (NMT) model based on the encoder-decoder model that incorporates named entity (NE) tags of source-language sentences. Conventional NMT models have two problems enumerated as follows: (i) they tend to have difficulty in translating words with multiple meanings because of the high ambiguity, and (ii) these models’abilitytotranslatecompoundwordsseemschallengingbecausetheencoderreceivesaword, a part of the compound word, at each time step. To alleviate these problems, the encoder of the proposed model encodes the input word on the basis of its NE tag at each time step, which could reduce the ambiguity of the input word. Furthermore,the encoder introduces a chunk-level LSTM layer over a word-level LSTM layer and hierarchically encodes a source-language sentence to capture a compound NE as a chunk on the basis of the NE tags. We evaluate the proposed model on an English-to-Japanese translation task with the ASPEC, and English-to-Bulgarian and English-to-Romanian translation tasks with the Europarl corpus. The evaluation results show that the proposed model achieves up to 3.11 point improvement in BLEU.

Recent neural models for response generation show good results in terms of general responses. In real conversations, however, depending on the speaker/responder, similar utterances should require different responses. In this study, we attempt to consider individual user’s information in adjusting the notable sequence-to-sequence (seq2seq) model for more diverse, user-specific responses. We assume that we need user-specific features to adjust the response and we argue that some selected representative words from the users are suitable for this task. Furthermore, we prove that even for unseen or unknown users, our model can provide more diverse and interesting responses, while maintaining correlation with input utterances. Experimental results with human evaluation show that our model can generate more interesting responses than the popular seq2seqmodel and achieve higher relevance with input utterances than our baseline.

Comments on a stock market often include the reason or cause of changes in stock prices, such as “Nikkei turns lower as yen’s rise hits exporters.” Generating such informative sentences requires capturing the relationship between different resources, including a target stock price. In this paper, we propose a model for automatically generating such informative market comments that refer to external resources. We evaluated our model through an automatic metric in terms of BLEU and human evaluation done by an expert in finance. The results show that our model outperforms the existing model both in BLEU scores and human judgment.

We focus on non-standard usages of common words on social media. In the context of social media, words sometimes have other usages that are totally different from their original. In this study, we attempt to distinguish non-standard usages on social media from standard ones in an unsupervised manner. Our basic idea is that non-standardness can be measured by the inconsistency between the expected meaning of the target word and the given context. For this purpose, we use context embeddings derived from word embeddings. Our experimental results show that the model leveraging the context embedding outperforms other methods and provide us with findings, for example, on how to construct context embeddings and which corpus to use.

In this research, we propose the task of question summarization. We first analyzed question-summary pairs extracted from a Community Question Answering (CQA) site, and found that a proportion of questions cannot be summarized by extractive approaches but requires abstractive approaches. We created a dataset by regarding the question-title pairs posted on the CQA site as question-summary pairs. By using the data, we trained extractive and abstractive summarization models, and compared them based on ROUGE scores and manual evaluations. Our experimental results show an abstractive method using an encoder-decoder model with a copying mechanism achieves better scores for both ROUGE-2 F-measure and the evaluations by human judges.

The sequence-to-sequence (Seq2Seq) model has been successfully applied to machine translation (MT). Recently, MT performances were improved by incorporating supervised attention into the model. In this paper, we introduce supervised attention to constituency parsing that can be regarded as another translation task. Evaluation results on the PTB corpus showed that the bracketing F-measure was improved by supervised attention.

This paper presents a novel encoder-decoder model for automatically generating market comments from stock prices. The model first encodes both short- and long-term series of stock prices so that it can mention short- and long-term changes in stock prices. In the decoding phase, our model can also generate a numerical value by selecting an appropriate arithmetic operation such as subtraction or rounding, and applying it to the input stock prices. Empirical experiments show that our best model generates market comments at the fluency and the informativeness approaching human-generated reference texts.

In English, high-quality sentence compression models by deleting words have been trained on automatically created large training datasets. We work on Japanese sentence compression by a similar approach. To create a large Japanese training dataset, a method of creating English training dataset is modified based on the characteristics of the Japanese language. The created dataset is used to train Japanese sentence compression models based on the recurrent neural network.

The majority of core techniques to solve many problems in Community Question Answering (CQA) task rely on similarity computation. This work focuses on similarity between two sentences (or questions in subtask B) based on word embeddings. We exploit words importance levels in sentences or questions for similarity features, for classification and ranking with machine learning. Using only 2 types of similarity metric, our proposed method has shown comparable results with other complex systems. This method on subtask B 2017 dataset is ranked on position 7 out of 13 participants. Evaluation on 2016 dataset is on position 8 of 12, outperforms some complex systems. Further, this finding is explorable and potential to be used as baseline and extensible for many tasks in CQA and other textual similarity based system.

We analyze semantic changes in loanwords from English that are used in Japanese (Japanese loanwords). Specifically, we create word embeddings of English and Japanese and map the Japanese embeddings into the English space so that we can calculate the similarity of each Japanese word and each English word. We then attempt to find loanwords that are semantically different from their original, see if known meaning changes are correctly captured, and show the possibility of using our methodology in language education.

We address the task of automatically estimating the missing values of linguistic features by making use of the fact that some linguistic features in typological databases are informative to each other. The questions to address in this work are (i) how much predictive power do features have on the value of another feature? (ii) to what extent can we attribute this predictive power to genealogical or areal factors, as opposed to being provided by tendencies or implicational universals? To address these questions, we conduct a discriminative or predictive analysis on the typological database. Specifically, we use a machine-learning classifier to estimate the value of each feature of each language using the values of the other features, under different choices of training data: all the other languages, or all the other languages except for the ones having the same origin or area with the target language.