Modeling Context in Answer Sentence Selection Systems on a Latency Budget
Rujun Han, Luca Soldaini, Alessandro Moschitti · 2021
Answer Sentence Selection (AS2) is an efficient approach for the design of open-domain Question Answering (QA) systems.In order to achieve low latency, traditional AS2 models score question-answer pairs individually, ignoring any information from the document each potential answer was extracted from.In contrast, more computationally expensive models designed for machine reading comprehension tasks typically receive one or more passages as input, which often results in better accuracy.In this work, we present an approach to efficiently incorporate contextual information in AS2 models.For each answer candidate, we first use unsupervised similarity techniques to extract relevant sentences from its source document, which we then feed into an efficient transformer architecture fine-tuned for AS2.Our best approach, which leverages a multi-way attention architecture to efficiently encode context, improves 6% to 11% over noncontextual state of the art in AS2 with minimal impact on system latency.All experiments in this work were conducted in English.* Work was conducted while the author was an intern at Amazon Alexa.The math of pi explained, as simply as possible How many digits of pi we really need?Pi, you may also remember from grade school, is not an ordinary number.It's irrational, meaning it has an endless number of decimals that never repeat.Though even cutting off pi at 15 digits allows for extremely precise measurements.If you were to draw a circle with a diameter of 25 billion miles, using 15 digits of pi, you'd only arrive at a measurement of the circumference that's off by 1.5 inches, NASA's Marc Rayman explained in a post on NASA's JPL website.And that's good enough.Of course, that hasn't stopped people from looking for more and more digits of pi.Currently, there are more than 22.4 trillion known digits, which show no hint of ending or repeating.Further reading: pi and pie.