MLPerf Inference Benchmark

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and five orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. In this paper, we present our benchmarking method for evaluating ML inference systems. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures. The first call for submissions garnered more than 600 reproducible inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities. The submissions attest to the benchmark's flexibility and adaptability.Comment: ISCA 202

DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation

There is an undeniable communication barrier between deaf people and people with normal hearing ability. Although innovations in sign language translation technology aim to tear down this communication barrier, the majority of existing sign language translation systems are either intrusive or constrained by resolution or ambient lighting conditions. Moreover, these existing systems can only perform single-sign ASL translation rather than sentence-level translation, making them much less useful in daily-life communication scenarios. In this work, we fill this critical gap by presenting DeepASL, a transformative deep learning-based sign language translation technology that enables ubiquitous and non-intrusive American Sign Language (ASL) translation at both word and sentence levels. DeepASL uses infrared light as its sensing mechanism to non-intrusively capture the ASL signs. It incorporates a novel hierarchical bidirectional deep recurrent neural network (HB-RNN) and a probabilistic framework based on Connectionist Temporal Classification (CTC) for word-level and sentence-level ASL translation respectively. To evaluate its performance, we have collected 7,306 samples from 11 participants, covering 56 commonly used ASL words and 100 ASL sentences. DeepASL achieves an average 94.5% word-level translation accuracy and an average 8.2% word error rate on translating unseen ASL sentences. Given its promising performance, we believe DeepASL represents a significant step towards breaking the communication barrier between deaf people and hearing majority, and thus has the significant potential to fundamentally change deaf people's lives

Skills and Profile of the New Role of the Translator as MT Post-editor

This paper explores the skills and profile of the new role of the translator as MT post-editor in view of the rising interest and use of MT in the translation industry. After a brief review of the relevant literature declaring post-editing (PE) as a profession on its own, the paper goes on to identify the different tasks involved in PE processes, following the work of Krings (Krings, 2001). Then, a series of competences are defined and grouped into three main categories: core competences, linguistic skills and instrumental competences. Finally, a description of the controlled translation scenario of MT PE is advanced taking into account the overall scenario of any translation project, including client description, text domain, text description, use of glossaries, MT engine, MT output quality and purpose of the translated text.Aquest article aborda les habilitats i les característiques del perfil del nou rol del traductor com a posteditor de traducció automàtica, tot i tenint en compte l'augment de l'interès en i l'ús de la traducció automàtica per part de la industria de la traducció. Després d'una breu revisió de la literatura més rellevant sobre postedició (PE) en tant que professió per ella mateixa, l'article identifica les diferents tasques implicades en els processos de PE, segons la proposta de Krings (2001). A continuació es defineix una sèrie de competències que s'agrupen en tres categories principals: competències nuclears, habilitats lingüístiques i competències instrumentals. Finalment el artículo proposa una descripció de l'escenari de traducció controlada propi de la PE de traducció automàtica, sense perdre de vista l'escenari general de qualsevol projecte de traducció, que inclou la descripció del client, el domini del text, la descripció del text, l'ús de glossaris, el motor de traducció automàtica, la qualitat de la traducció automàtica resultant i el propòsit del text traduït.Este artículo aborda las habilidades y las características del perfil del nuevo rol del traductor como poseditor de traducción automática, a la luz del aumento del interés en y del uso de la traducción automática por parte de la industria de la traducción. Después de una breve revisión de la literatura más relevante sobre posedición (PE) en tanto que profesión por sí misma, en el artículo se identifican las diferentes tareas implicadas en los procesos de PE, según la propuesta de Krings (2001). A continuación se define una serie de competencias que se agrupan en tres categorías principales: competencias nucleares, habilidades lingüísticas y competencias instrumentales. Finalmente el artículo propone una descripción del escenario de traducción controlada propio de la PE de traducción automática, sin perder de vista el marco general de cualquier proyecto de traducción, que incluye la descripción del cliente, el dominio del texto, la descripción del texto, el uso de glosarios, el motor de traducción automática, la calidad de la traducción automática resultante y el propósito del texto traducido