Multilingual Universal Sentence Encoder for Semantic Retrieval

Yang, Yinfei; Cer, Daniel; Amin, Ahmad; Guo, Mandy; Law, Jax; Constant, Noah; Ábrego, Gustavo Hernández; Yuan, Steve; Tar, Chris; Sung, Yun-Hsuan; Strope, Brian; Kurzweil, Ray

doi:10.18653/v1/2020.acl-demos.12

Cited by 241 publications

(161 citation statements)

References 13 publications

(29 reference statements)

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“…We hypothesized that part of this encoded meaning includes information about compounds mentioned within text, and that we could map a compound into semantic space by calculating the centroid of the document vectors in which it occurs ( Figure 1A). To first assemble document embeddings, we employed the pre-trained Universal Sentence Encoder multilingual model on ~14.6M Pubmed abstracts [11]. We assessed the quality of the document embeddings using a derived Information Retrieval task, and obtained an accuracy of 90.5%, a 26.4% improvement over a bag-of-words approach (Supplementary Table 1).…”

Section: Resultsmentioning

confidence: 99%

Document Embedding Centroids: New and Versatile Semantic Descriptors for Compounds

Maria

Gleim²,

Lounkine³

et al. 2020

Preprint

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Section: Resultsmentioning

confidence: 99%

Document Embedding Centroids: New and Versatile Semantic Descriptors for Compounds

Maria

Gleim²,

Lounkine³

et al. 2020

Preprint

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“…In automated approaches, we used three types of text representation: bag of words, dictionary vectors (POS + LCM + Sentiment), and utterance embedding vectors obtained from the Universal Sentence Encoder (USE) [44]. We then used those representations with multiple types of algorithms to classify texts:…”

Section: Methodsmentioning

confidence: 99%

“…One of the methods used to obtain text representations was the USE [44]. This deep neural network text encoder supports 16 languages, among them Polish.…”

Section: Universal Sentence Encoder (Use) Text Representationsmentioning

confidence: 99%

Single and Cross-Disorder Detection for Autism and Schizophrenia

et al. 2021

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Detection of mental disorders from textual input is an emerging field for applied machine and deep learning methods. Here, we explore the limits of automated detection of autism spectrum disorder (ASD) and schizophrenia (SCZ). We compared the performance of: (1) dedicated diagnostic tools that involve collecting textual data, (2) automated methods applied to the data gathered by these tools, and (3) psychiatrists. Our article tests the effectiveness of several baseline approaches, such as bag of words and dictionary-based vectors, followed by a machine learning model. We employed two more refined Sentic text representations using affective features and concept-level analysis on texts. Further, we applied selected state-of-the-art deep learning methods for text representation and inference, as well as experimented with transfer and zero-shot learning. Finally, we also explored few-shot methods dedicated to low data size scenarios, which is a typical problem for the clinical setting. The best breed of automated methods outperformed human raters (psychiatrists). Cross-dataset approaches turned out to be useful (only from SCZ to ASD) despite different data types. The few-shot learning methods revealed promising results on the SCZ dataset. However, more effort is needed to explore the approaches to efficiently training models, given the very limited amounts of labeled clinical data. Psychiatry is one of the few medical fields in which the diagnosis of most disorders is based on the subjective assessment of a psychiatrist. Therefore, the introduction of objective tools supporting diagnostics seems to be pivotal. This paper is a step in this direction.

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“…Despite its well-known criticalities, Achinstein's model seemed to us the most suitable among all for building a user-centric explanatory software, by suggesting that explaining is akin to Question Answering (QA). On this aspect, one of the main technological limitations of state-of-the-art automated QA [24,31] is that it tends to lose its effectiveness when the questions are too broad. This results in several issues when the user has no knowledge of the domain, thus forcing him/her to resort to generic questions in order to acquire enough information to be able to approach more specific questions.…”

Section: Proposed Solutionmentioning

confidence: 99%

“…Despite its criticalities, Achinstein's theory seemed to us the most suitable among all, for our purposes, for allowing the assessment of the quality of an explanation/answer on the base of its pragmatic relevance to a question. A task that may seem too onerous and subjective, nonetheless recent developments in modern artificial intelligence have shown there might exist tools [24,31] to objectively estimate the pertinence of an answer thus allowing for the automation of a question answering process.…”

Section: Introductionmentioning

confidence: 99%

From Philosophy to Interfaces: an Explanatory Method and a Tool Inspired by Achinstein’s Theory of Explanation

Sovrano

Vitali

2021

26th International Conference on Intelligent User Interfaces

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We propose a new method for explanations in Artificial Intelligence (AI) and a tool to test its expressive power within a user interface. In order to bridge the gap between philosophy and humancomputer interfaces, we show a new approach for the generation of interactive explanations based on a sophisticated pipeline of AI algorithms for structuring natural language documents into knowledge graphs, answering questions effectively and satisfactorily. Among the mainstream philosophical theories of explanation we identified one that in our view is more easily applicable as a practical model for user-centric tools: Achinstein's Theory of Explanation. With this work we aim to prove that the theory proposed by Achinstein can be actually adapted for being implemented into a concrete software application, as an interactive process answering questions. To this end we found a way to handle the generic (archetypal) questions that implicitly characterise an explanatory processes as preliminary overviews rather than as answers to explicit questions, as commonly understood. To show the expressive power of this approach we designed and implemented a pipeline of AI algorithms for the generation of interactive explanations under the form of overviews, focusing on this aspect of explanations rather than on existing interfaces and presentation logic layers for question answering. Accordingly, through the identification of a minimal set of archetypal questions it is possible to create a generator of explanatory overviews that is generic enough to significantly ease the acquisition of knowledge by humans, regardless of the specificities of the users outside of a minimum set of very broad requirements (e.g. people able to read and understand English and capable of performing basic common-sense reasoning). We tested our hypothesis on a well-known XAI-powered credit approval system by IBM, comparing CEM, a static explanatory tool for post-hoc explanations, with an extension we developed adding interactive explanations based on our model. The results of the user study, involving more than 100 participants, showed that our proposed solution produced a statistically relevant improvement on effectiveness (U=931.0, p=0.036) over the baseline, thus giving evidence in favour of our theory.

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Multilingual Universal Sentence Encoder for Semantic Retrieval

Cited by 241 publications

References 13 publications

Document Embedding Centroids: New and Versatile Semantic Descriptors for Compounds

Document Embedding Centroids: New and Versatile Semantic Descriptors for Compounds

Single and Cross-Disorder Detection for Autism and Schizophrenia

From Philosophy to Interfaces: an Explanatory Method and a Tool Inspired by Achinstein’s Theory of Explanation

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