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Amazon’s 23 papers at EMNLP 2021

Natural-language understanding and question answering are areas of focus, with additional topics ranging from self-learning to text summarization.

By Larry Hardesty
November 5, 2021
5 min read
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Of the 23 papers that Amazon researchers are presenting at next week's Conference on Empirical Methods in Natural Language Processing (EMNLP), the majority concentrate on two topics: natural-language understanding, or the semantic interpretation of text, and question answering, both of which are important across Amazon businesses, including Alexa, Amazon Web Services, and the Amazon Store. 

The remaining 10 papers cover a range of topics, from self-learning and information retrieval to language modeling and machine translation.

MetaTS.png
The framework of the meta teacher-student network (MetaTS), a teacher-student framework that allows the teacher to dynamically adapt its pseudoannotation strategies by the student’s feedback. Figure from "MetaTS: Meta teacher-student network for multilingual sequence labeling with minimal supervision".

Within the area of natural-language understanding, Amazon researchers apply a battery of techniques — such as semi-supervised learning, few-shot learning, and contrastive learning — to a variety of subproblems, such as visual referring-expression recognition, or identifying which object in an image a natural-language expression refers to; coreference resolution, or determining whether different terms refer to the same entity; and dealing with distribution shift, or a mismatch between the distribution of data at inference time and the distribution in the training set.

Amazon researchers’ work on question answering includes helping conversational-AI agents suggest follow-up questions during interactions with customers; filtration of unanswerable questions to prevent the waste of system resources; and few-shot learning.

Few-shot.png
A new approach to few-shot learning for question answering formulates the task as masked span filling during fine-tuning. This enables the use of the pretraining objective during fine-tuning, making the system extremely sample efficient. Top: Pretraining framework; middle: existing fine-tuning frameworks; bottom: proposed fine-tuning framework. Figure from "FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models".

Amazon Web Services researchers address questions of fairness in a paper on mitigating gender bias in machine translation models.

In the area of information retrieval, Amazon papers investigate an integrated model for conversational search and the identification of counterfactual claims in product reviews that can create a misleading impression of the reviewer’s sentiment.

A pair of Amazon papers look at the type of language modeling that accounts for so much of the recent success of natural-language-processing models.

Alexa researchers combined data mixing and elastic weight consolidation to improve the adaptation of machine translation models to new tasks.

Paraphrase generation varies the surface form of sentences while preserving their semantic content, so it can help augment training data for other natural-language-processing tasks.

Self-learning is the use of implicit feedback signals to automatically improve machine learning models, without the need for human intervention.

Implicit feedback.png
Interrupting a conversational-AI agent to rephrase a request provides an implicit-feedback signal that can be used to automatically label training data, which can help improve the underlying machine learning model. Figure from "A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems".

Text summarization is a widely studied problem in natural-language processing, and a new paper from Amazon Web Services considers the particular problems it presents in the context of dialogue.

For more on Amazon's involvement at EMNLP, see our interview with Georgiana Dinu, an applied scientist with Amazon Web Services and a conference area chair for machine learning for natural-language-processing.

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About the Author
Larry Hardesty
Larry Hardesty is the editor of the Amazon Science blog. Previously, he was a senior editor at MIT Technology Review and the computer science writer at the MIT News Office.

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