Dense retrieval aims at searching for the most relevant documents to the given query by encoding texts in the embedding space, requiring a large amount of query-document pairs to train. Since manually constructing such training data is challenging, recent work has proposed to generate synthetic queries from documents and use them to train a dense retriever. However, compared to the manually composed queries, synthetic queries do not generally ask for implicit information, therefore leading to a degraded retrieval performance. In this work, we propose Query Generation with External Knowledge (QGEK), a novel method for generating queries with external information related to the corresponding document. Specifically, we convert a query into a triplet-based template form to accommodate external information and transmit it to a pre-trained language model (PLM). We validate QGEK on both in-domain and out-domain dense retrieval settings. The dense retriever with the queries requiring implicit information is found to make good performance improvement. Also, such queries are similar to manually composed queries, confirmed by both human evaluation and unique & non-unique words distribution.
Language identification is one of the fundamental tasks in natural language processing that is a prerequisite to data processing and numerous applications. Low-resourced languages with similar typologies are generally confused with each other in real-world applications such as machine translation, affecting the user’s experience. In this work, we present a language identification dataset for five typologically and phylogenetically related low-resourced East African languages that use the Ge’ez script as a writing system; namely Amharic, Blin, Ge’ez, Tigre, and Tigrinya. The dataset is built automatically from selected data sources, but we also performed a manual evaluation to assess its quality. Our approach to constructing the dataset is cost-effective and applicable to other low-resource languages. We integrated the dataset into an existing language-identification tool and also fine-tuned several Transformer based language models, achieving very strong results in all cases. While the task of language identification is easy for the informed person, such datasets can make a difference in real-world deployments and also serve as part of a benchmark for language understanding in the target languages. The data and models are made available at https://github.com/fgaim/geezswitch.
Evaluating the quality of responses generated by open-domain conversation systems is a challenging task. This is partly because there can be multiple appropriate responses to a given dialogue history. Reference-based metrics that rely on comparisons to a set of known correct responses often fail to account for this variety, and consequently correlate poorly with human judgment. To address this problem, researchers have investigated the possibility of assessing response quality without using a set of known correct responses. RUBER demonstrated that an automatic response evaluation model could be made using unsupervised learning for the next-utterance prediction (NUP) task. For the unsupervised learning of such model, we propose a method of manipulating a golden response to create a new negative response that is designed to be inappropriate within the context while maintaining high similarity with the original golden response. We find, from our experiments on English datasets, that using the negative samples generated by our method alongside random negative samples can increase the model’s correlation with human evaluations. The process of generating such negative samples is automated and does not rely on human annotation.
Annotation quality control is a critical aspect for building reliable corpora through linguistic annotation. In this study, we present a simple but powerful quality control method using two-step reason selection. We gathered sentential annotations of local acceptance and three related attributes through a crowdsourcing platform. For each attribute, the reason for the choice of the attribute value is selected in a two-step manner. The options given for reason selection were designed to facilitate the detection of a nonsensical reason selection. We assume that a sentential annotation that contains a nonsensical reason is less reliable than the one without such reason. Our method, based solely on this assumption, is found to retain the annotations with satisfactory quality out of the entire annotations mixed with those of low quality.
Considering diverse aspects of an argumentative issue is an essential step for mitigating a biased opinion and making reasonable decisions. A related generation model can produce flexible results that cover a wide range of topics, compared to the retrieval-based method that may show unstable performance for unseen data. In this paper, we study the problem of generating sentential arguments from multiple perspectives, and propose a neural method to address this problem. Our model, ArgDiver (Argument generation model from diverse perspectives), in a way a conversational system, successfully generates high-quality sentential arguments. At the same time, the automatically generated arguments by our model show a higher diversity than those generated by any other baseline models. We believe that our work provides evidence for the potential of a good generation model in providing diverse perspectives on a controversial topic.
We propose a method of machine-assisted annotation for the identification of tension development, annotating whether the tension is increasing, decreasing, or staying unchanged. We use a neural network based prediction model, whose predicted results are given to the annotators as initial values for the options that they are asked to choose. By presenting such initial values to the annotators, the annotation task becomes an evaluation task where the annotators inspect whether or not the predicted results are correct. To demonstrate the effectiveness of our method, we performed the annotation task in both in-house and crowdsourced environments. For the crowdsourced environment, we compared the annotation results with and without our method of machine-assisted annotation. We find that the results with our method showed a higher agreement to the gold standard than those without, though our method had little effect at reducing the time for annotation. Our codes for the experiment are made publicly available.
Genetic information in the literature has been extensively looked into for the purpose of discovering the etiology of a disease. As the gene-disease relation is sensitive to external factors, their identification is important to study a disease. Environmental influences, which are usually called Gene-Environment interaction (GxE), have been considered as important factors and have extensively been researched in biology. Nevertheless, there is still a lack of systems for automatic GxE extraction from the biomedical literature due to new challenges: (1) there are no preprocessing tools and corpora for GxE, (2) expressions of GxE are often quite implicit, and (3) document-level comprehension is usually required. We propose to overcome these challenges with neural network models and show that a modified sequence-to-sequence model with a static RNN decoder produces a good performance in GxE recognition.