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A Convolutional Neural Network Based Approach to Recognize Bangla Spoken Digits from Speech Signal ...
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Thought Flow Nets: From Single Predictions to Trains of Model Thought ...
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Comparing Approaches to Dravidian Language Identification ...
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Speaking clearly improves speech segmentation by statistical learning under optimal listening conditions
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In: Laboratory Phonology: Journal of the Association for Laboratory Phonology; Vol 12, No 1 (2021); 14 ; 1868-6354 (2021)
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Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks
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In: https://hal.archives-ouvertes.fr/hal-03429051 ; [Research Report] LACITO (UMR 7107). 2021 (2021)
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Translating the Unseen? Yoruba-English MT in Low-Resource, Morphologically-Unmarked Settings ...
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MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System ...
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Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales ...
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Contextual Sentence Classification: Detecting Sustainability Initiatives in Company Reports ...
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Tripartitions of the first person space (Tamil speakers, Condition 1) ...
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Compositional Processing Emerges in Neural Networks Solving Math Problems ...
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Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning ...
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Abstract:
Imagine you are in a supermarket. You have two bananas in your basket and want to buy four apples. How many fruits do you have in total? This seemingly straightforward question can be challenging for data-driven language models, even if trained at scale. However, we would expect such generic language models to possess some mathematical abilities in addition to typical linguistic competence. Towards this goal, we investigate if a commonly used language model, BERT, possesses such mathematical abilities and, if so, to what degree. For that, we fine-tune BERT on a popular dataset for word math problems, AQuA-RAT, and conduct several tests to understand learned representations better. Since we teach models trained on natural language to do formal mathematics, we hypothesize that such models would benefit from training on semi-formal steps that explain how math results are derived. To better accommodate such training, we also propose new pretext tasks for learning mathematical rules. We call them (Neighbor) ... : The paper has been accepted to the ACL-IJCNLP 2021 conference ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; I.2.7; Machine Learning cs.LG
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URL: https://arxiv.org/abs/2106.03921 https://dx.doi.org/10.48550/arxiv.2106.03921
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An empirical analysis of phrase-based and neural machine translation ...
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ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning ...
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Updater-Extractor Architecture for Inductive World State Representations ...
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Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? ...
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multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning ...
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Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines ...
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Exceeding the Limits of Visual-Linguistic Multi-Task Learning ...
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