Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German

The goal of this work is to design a machine translation (MT) system for a low-resource family of dialects, collectively known as Swiss German, which are widely spoken in Switzerland but seldom written. We collected a significant number of parallel written resources to start with, up to a total of about 60k words. Moreover, we identified several other promising data sources for Swiss German. Then, we designed and compared three strategies for normalizing Swiss German input in order to address the regional diversity. We found that character-based neural MT was the best solution for text normalization. In combination with phrase-based statistical MT, our solution reached 36% BLEU score when translating from the Bernese dialect. This value, however, decreases as the testing data becomes more remote from the training one, geographically and topically. These resources and normalization techniques are a first step towards full MT of Swiss German dialects.Comment: 11th Language Resources and Evaluation Conference (LREC), 7-12 May 2018, Miyazaki (Japan

Scalable ASL sign recognition using model-based machine learning and linguistically annotated corpora

We report on the high success rates of our new, scalable, computational approach for sign recognition from monocular video, exploiting linguistically annotated ASL datasets with multiple signers. We recognize signs using a hybrid framework combining state-of-the-art learning methods with features based on what is known about the linguistic composition of lexical signs. We model and recognize the sub-components of sign production, with attention to hand shape, orientation, location, motion trajectories, plus non-manual features, and we combine these within a CRF framework. The effect is to make the sign recognition problem robust, scalable, and feasible with relatively smaller datasets than are required for purely data-driven methods. From a 350-sign vocabulary of isolated, citation-form lexical signs from the American Sign Language Lexicon Video Dataset (ASLLVD), including both 1- and 2-handed signs, we achieve a top-1 accuracy of 93.3% and a top-5 accuracy of 97.9%. The high probability with which we can produce 5 sign candidates that contain the correct result opens the door to potential applications, as it is reasonable to provide a sign lookup functionality that offers the user 5 possible signs, in decreasing order of likelihood, with the user then asked to select the desired sign

Improving Machine Translation of Educational Content via Crowdsourcing

The limited availability of in-domain training data is a major issue in the training of application-specific neural machine translation models. Professional outsourcing of bilingual data collections is costly and often not feasible. In this paper we analyze the influence of using crowdsourcing as a scalable way to obtain translations of target in-domain data having in mind that the translations can be of a lower quality. We apply crowdsourcing with carefully designed quality controls to create parallel corpora for the educational domain by collecting translations of texts from MOOCs from English to eleven languages, which we then use to fine-tune neural machine translation models previously trained on general-domain data. The results from our research indicate that crowdsourced data collected with proper quality controls consistently yields performance gains over general-domain baseline systems, and systems fine-tuned with pre-existing in-domain corpora

Designing a Collaborative Process to Create Bilingual Dictionaries of Indonesian Ethnic Languages

The constraint-based approach has been proven useful for inducing bilingual dictionary for closely-related low-resource languages. When we want to create multiple bilingual dictionaries linking several languages, we need to consider manual creation by a native speaker if there are no available machine-readable dictionaries are available as input. To overcome the difficulty in planning the creation of bilingual dictionaries, the consideration of various methods and costs, plan optimization is essential. Utilizing both constraint-based approach and plan optimizer, we design a collaborative process for creating 10 bilingual dictionaries from every combination of 5 languages, i.e., Indonesian, Malay, Minangkabau, Javanese, and Sundanese. We further design an online collaborative dictionary generation to bridge spatial gap between native speakers. We define a heuristic plan that only utilizes manual investment by the native speaker to evaluate our optimal plan with total cost as an evaluation metric. The optimal plan outperformed the heuristic plan with a 63.3% cost reduction

Designing a Collaborative Process to Create Bilingual Dictionaries of Indonesian Ethnic Languages

The constraint-based approach has been proven useful for inducing bilingual dictionary for closely-related low-resource languages. When we want to create multiple bilingual dictionaries linking several languages, we need to consider manual creation by a native speaker if there are no available machine-readable dictionaries are available as input. To overcome the difficulty in planning the creation of bilingual dictionaries, the consideration of various methods and costs, plan optimization is essential. Utilizing both constraint-based approach and plan optimizer, we design a collaborative process for creating 10 bilingual dictionaries from every combination of 5 languages, i.e., Indonesian, Malay, Minangkabau, Javanese, and Sundanese. We further design an online collaborative dictionary generation to bridge spatial gap between native speakers. We define a heuristic plan that only utilizes manual investment by the native speaker to evaluate our optimal plan with total cost as an evaluation metric. The optimal plan outperformed the heuristic plan with a 63.3% cost reduction

Designing a Collaborative Process to Create Bilingual Dictionaries of Indonesian Ethnic Languages

The constraint-based approach has been proven useful for inducing bilingual dictionary for closely-related low-resource languages. When we want to create multiple bilingual dictionaries linking several languages, we need to consider manual creation by a native speaker if there are no available machine-readable dictionaries are available as input. To overcome the difficulty in planning the creation of bilingual dictionaries, the consideration of various methods and costs, plan optimization is essential. Utilizing both constraint-based approach and plan optimizer, we design a collaborative process for creating 10 bilingual dictionaries from every combination of 5 languages, i.e., Indonesian, Malay, Minangkabau, Javanese, and Sundanese. We further design an online collaborative dictionary generation to bridge spatial gap between native speakers. We define a heuristic plan that only utilizes manual investment by the native speaker to evaluate our optimal plan with total cost as an evaluation metric. The optimal plan outperformed the heuristic plan with a 63.3% cost reduction