DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation

In previous works, only parameter weights of ASR models are optimized under fixed-topology architecture. However, the design of successful model architecture has always relied on human experience and intuition. Besides, many hyperparameters related to model architecture need to be manually tuned. Therefore in this paper, we propose an ASR approach with efficient gradient-based architecture search, DARTS-ASR. In order to examine the generalizability of DARTS-ASR, we apply our approach not only on many languages to perform monolingual ASR, but also on a multilingual ASR setting. Following previous works, we conducted experiments on a multilingual dataset, IARPA BABEL. The experiment results show that our approach outperformed the baseline fixed-topology architecture by 10.2% and 10.0% relative reduction on character error rates under monolingual and multilingual ASR settings respectively. Furthermore, we perform some analysis on the searched architectures by DARTS-ASR.Comment: Accepted at INTERSPEECH 202

Lessons learned in multilingual grounded language learning

Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language. Here, we investigate in detail which conditions affect the performance of this type of grounded language learning model. We show that multilingual training improves over bilingual training, and that low-resource languages benefit from training with higher-resource languages. We demonstrate that a multilingual model can be trained equally well on either translations or comparable sentence pairs, and that annotating the same set of images in multiple language enables further improvements via an additional caption-caption ranking objective.Comment: CoNLL 201

One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis

When learning a new skill, you take advantage of your preexisting skills and knowledge. For instance, if you are a skilled violinist, you will likely have an easier time learning to play cello. Similarly, when learning a new language you take advantage of the languages you already speak. For instance, if your native language is Norwegian and you decide to learn Dutch, the lexical overlap between these two languages will likely benefit your rate of language acquisition. This thesis deals with the intersection of learning multiple tasks and learning multiple languages in the context of Natural Language Processing (NLP), which can be defined as the study of computational processing of human language. Although these two types of learning may seem different on the surface, we will see that they share many similarities. The traditional approach in NLP is to consider a single task for a single language at a time. However, recent advances allow for broadening this approach, by considering data for multiple tasks and languages simultaneously. This is an important approach to explore further as the key to improving the reliability of NLP, especially for low-resource languages, is to take advantage of all relevant data whenever possible. In doing so, the hope is that in the long term, low-resource languages can benefit from the advances made in NLP which are currently to a large extent reserved for high-resource languages. This, in turn, may then have positive consequences for, e.g., language preservation, as speakers of minority languages will have a lower degree of pressure to using high-resource languages. In the short term, answering the specific research questions posed should be of use to NLP researchers working towards the same goal.Comment: PhD thesis, University of Groninge