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NLPMC 2023 Workshop on NLP for Medical Conversations2023In clinical visits, clinical note writing is a timeconsuming and cost-prohibitive manual task for clinicians. Although virtual medical scribes have been proposed to generate clinical notes (semi-)automatically, the data sparsity issue is still a challenging problem in practice. Identifying the topic of clinical utterances in doctorpatient conversations is one of the key strategies for automation. In this
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EMNLP 20232023Product question answering (PQA) aims to provide instant responses to customer questions posted on shopping message boards, social media, brand websites and retail stores. In this paper, we propose a distantly supervised solution to answer customer questions by using product information. Auto-answering questions using product information poses two main challenges: (i) labelled data is not readily available
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EMNLP 20232023End-to-end multilingual entity linking (MEL) is concerned with identifying multilingual entity mentions and their corresponding entity IDs in a knowledge base. Prior efforts assume that entity mentions are given and skip the entity mention detection step due to a lack of high-quality multilingual training corpora. To overcome this limitation, we propose mReFinED, the first end-to-end MEL model. Additionally
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KDD 2024, NeurIPS 2023 Workshop on Distribution Shifts (DistShifts)2023Pre-trained language models (PLMs) have seen tremendous success in text classification (TC) problems in the context of Natural Language Processing (NLP). In many real-world text classification tasks, the class definitions being learned do not remain constant but rather change with time - this is known as concept shift. Most techniques for handling concept shift rely on retraining the old classifiers with
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ASRU 20232023In audiovisual speech recognition (AV-ASR), for many languages only few audiovisual data is available. Building upon an English model, in this work, we first apply and analyze various adapters for cross-language transfer learning to build a parameter-efficient and easy-to-extend AV-ASR in multiple languages. Fine-tuning only the bottleneck adapter with 4% of encoder’s parameters and the decoder shows comparable
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June 7, 2018Alexa is a cloud-based service with natural-language-understanding capabilities that powers devices like Amazon Echo, Echo Show, Echo Plus, Echo Spot, Echo Dot, and more. Alexa-like voice services traditionally have supported small numbers of well-separated domains, such as calendar or weather. In an effort to extend the capabilities of Alexa, Amazon in 2015 released the Alexa Skills Kit, so third-party developers could add to Alexa’s voice-driven capabilities. We refer to new third-party capabilities as skills, and Alexa currently has more than 40,000. -
June 1, 2018Developing a new Alexa skill typically means training a machine-learning system with annotated data, and the skill’s ability to “understand” natural-language requests is limited by the expressivity of the semantic representation used to do the annotation. So far, the techniques used to represent natural language have been fairly simple, so Alexa has been able to handle only relatively simple requests.
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May 29, 2018As Alexa-enabled devices continue to expand into new countries, we propose an approach for quickly bootstrapping machine-learning models in new languages, with the aim of more efficiently bringing Alexa to new customers around the world.
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May 24, 2018Amazon scientists are continuously expanding Alexa’s natural-language-understanding (NLU) capabilities to make Alexa smarter, more useful, and more engaging.
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Yang, JunMay 11, 2018Smart speakers, such as the Amazon Echo family of products, are growing in popularity among consumer and business audiences. In order to improve the automatic speech recognition (ASR) and full-duplex voice communication (FDVC) performance of these smart speakers, acoustical echo cancellation (AEC) and noise reduction systems are required. These systems reduce the noises and echoes that can impact operation, such as an Echo device accurately hearing the wake word “Alexa.”
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May 4, 2018In recent years, the amount of textual information produced daily has increased exponentially. This information explosion has been accelerated by the ease with which data can be shared across the web. Most of the textual information is generated as free-form text, and only a small fraction is available in structured format (Wikidata, Freebase etc.) that can be processed and analyzed directly by machines.
