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Register a new userAttention Weights in Transformer NMT Fail Aligning Words Between Sequences but Largely Explain Model Predictions
الاهتمام للأوزان في محول NMT فشل محاذاة الكلمات بين التسلسلات ولكن شرح تنبؤات نموذج إلى حد كبير
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يقترح هذا العمل تحليلا مكثفا للهندسة المعمارية المحول في إعداد الترجمة الآلية العصبية (NMT).مع التركيز على آلية اهتمام التشفير في فك التشفير، نثبت أن أوزان الاهتمام بانتظام أخطاء المحاذاة من خلال الاعتماد بشكل أساسي على الرموز غير المصنفة من تسلسل المصدر.ومع ذلك، نلاحظ أن نماذج NMT تخصص الاهتمام بهؤلاء الرموز لتنظيم المساهمة في التنبؤ بالسياقتين المصدرين وبادئة التسلسل المستهدف.نحن نقدم دليلا على تأثير محاذاة خاطئة على السلوك النموذجي، مما يدل على أن آلية اهتمام فك تشفير التشفير مفاجأة بشكل جيد كطريقة الترجمة الترجمة الترجمة الشخصية ل NMT.أخيرا، استنادا إلى تحليلنا، نقترح طرق تقلل إلى حد كبير معدل خطأ محاذاة الكلمة مقارنة بالمحاذاة المستحثة القياسية من أوزان الاهتمام.
This work proposes an extensive analysis of the Transformer architecture in the Neural Machine Translation (NMT) setting. Focusing on the encoder-decoder attention mechanism, we prove that attention weights systematically make alignment errors by relying mainly on uninformative tokens from the source sequence. However, we observe that NMT models assign attention to these tokens to regulate the contribution in the prediction of the two contexts, the source and the prefix of the target sequence. We provide evidence about the influence of wrong alignments on the model behavior, demonstrating that the encoder-decoder attention mechanism is well suited as an interpretability method for NMT. Finally, based on our analysis, we propose methods that largely reduce the word alignment error rate compared to standard induced alignments from attention weights.
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We offer an approach to explain Decision Tree (DT) predictions by addressing potential conflicts between aspects of these predictions and plausible expectations licensed by background information. We define four types of conflicts, operationalize the
ir identification, and specify explanatory schemas that address them. Our human evaluation focused on the effect of explanations on users' understanding of a DT's reasoning and their willingness to act on its predictions. The results show that (1) explanations that address potential conflicts are considered at least as good as baseline explanations that just follow a DT path; and (2) the conflict-based explanations are deemed especially valuable when users' expectations disagree with the DT's predictions.
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Earning calls are among important resources for investors and analysts for updating their price targets. Firms usually publish corresponding transcripts soon after earnings events. However, raw transcripts are often too long and miss the coherent str
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