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Register a new userBeyond the Tip of the Iceberg: Assessing Coherence of Text Classifiers
ما وراء طرف جبل الجليد: تقييم تماسك نصوص النصوص
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نظرا لأن طرازات لغة واسعة النطاق مدربة مسبقا تحقق دقة على المستوى البشري والأخبار الدقة على مهام فهم اللغة الحالية، دعت التحيز الإحصائي في البيانات القياسية والدراسات التحقيق مؤخرا إلى قدراتهم الحقيقية.للحصول على تقييم أكثر إعلانية من الدقة بشأن مهام تصنيف النص يمكن أن تقدم، نقترح تقييم أنظمة من خلال مقياس جديد لتماسك التنبؤ.نحن نطبق إطار عملنا على اثنين من التفاهم مع المعايير ذات الخصائص المختلفة لإظهار تنوعه.تبين نتائجنا التجريبية أن إطار التقييم هذا، على الرغم من بسيطة في الأفكار والتنفيذ، هو إجراء سريع وفعال وتنوعا لتوفير نظرة ثاقبة في تماسك تنبؤات الآلات.
As large-scale, pre-trained language models achieve human-level and superhuman accuracy on existing language understanding tasks, statistical bias in benchmark data and probing studies have recently called into question their true capabilities. For a more informative evaluation than accuracy on text classification tasks can offer, we propose evaluating systems through a novel measure of prediction coherence. We apply our framework to two existing language understanding benchmarks with different properties to demonstrate its versatility. Our experimental results show that this evaluation framework, although simple in ideas and implementation, is a quick, effective, and versatile measure to provide insight into the coherence of machines' predictions.
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Many existing approaches for interpreting text classification models focus on providing importance scores for parts of the input text, such as words, but without a way to test or improve the interpretation method itself. This has the effect of compou
nding the problem of understanding or building trust in the model, with the interpretation method itself adding to the opacity of the model. Further, importance scores on individual examples are usually not enough to provide a sufficient picture of model behavior. To address these concerns, we propose MOXIE (MOdeling conteXt-sensitive InfluencE of words) with an aim to enable a richer interface for a user to interact with the model being interpreted and to produce testable predictions. In particular, we aim to make predictions for importance scores, counterfactuals and learned biases with MOXIE. In addition, with a global learning objective, MOXIE provides a clear path for testing and improving itself. We evaluate the reliability and efficiency of MOXIE on the task of sentiment analysis.
The research aims to identify the degree of knowledge and
recruit teachers for thinking strategies beyond the knowledge in the
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owledge and
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by the valiant minor for high achievers, as applied to them to
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beyond the cognitive, which are prepared by the researcher after
verifying the validity and reliability.
Understanding when a text snippet does not provide a sought after information is an essential part of natural language utnderstanding. Recent work (SQuAD 2.0; Rajpurkar et al., 2018) has attempted to make some progress in this direction by enriching
the SQuAD dataset for the Extractive QA task with unanswerable questions. However, as we show, the performance of a top system trained on SQuAD 2.0 drops considerably in out-of-domain scenarios, limiting its use in practical situations. In order to study this we build an out-of-domain corpus, focusing on simple event-based questions and distinguish between two types of IDK questions: competitive questions, where the context includes an entity of the same type as the expected answer, and simpler, non-competitive questions where there is no entity of the same type in the context. We find that SQuAD 2.0-based models fail even in the case of the simpler questions. We then analyze the similarities and differences between the IDK phenomenon in Extractive QA and the Recognizing Textual Entailments task (RTE; Dagan et al., 2013) and investigate the extent to which the latter can be used to improve the performance.
Quality Estimation (QE) plays an essential role in applications of Machine Translation (MT). Traditionally, a QE system accepts the original source text and translation from a black-box MT system as input. Recently, a few studies indicate that as a b
y-product of translation, QE benefits from the model and training data's information of the MT system where the translations come from, and it is called the glass-box QE''. In this paper, we extend the definition of glass-box QE'' generally to uncertainty quantification with both black-box'' and glass-box'' approaches and design several features deduced from them to blaze a new trial in improving QE's performance. We propose a framework to fuse the feature engineering of uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. Experiment results show that our method achieves state-of-the-art performances on the datasets of WMT 2020 QE shared task.
Dialogue topic segmentation is critical in several dialogue modeling problems. However, popular unsupervised approaches only exploit surface features in assessing topical coherence among utterances. In this work, we address this limitation by leverag
ing supervisory signals from the utterance-pair coherence scoring task. First, we present a simple yet effective strategy to generate a training corpus for utterance-pair coherence scoring. Then, we train a BERT-based neural utterance-pair coherence model with the obtained training corpus. Finally, such model is used to measure the topical relevance between utterances, acting as the basis of the segmentation inference. Experiments on three public datasets in English and Chinese demonstrate that our proposal outperforms the state-of-the-art baselines.
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