Human annotation for establishing the training data is often a very costly process in natural language processing (NLP) tasks, which has led to frugal NLP approaches becoming an important research topic. Many research teams struggle to complete projects with limited funding, labor, and computational resources. Driven by the Move-Step analytic framework theorized in the applied linguistics field, our study offers a rigorous approach to the frugal use of two human annotators to scale up auto-coding for text classification tasks. We applied the Linear Support Vector Machine algorithm to text classification of a job ad corpus. Our Cohen’s Kappa for inter-rater agreement and Area Under the Curve (AUC) values reached averages of 0.76 and 0.80, respectively. The calculated time consumption for our human training process was 36 days. The results indicated that even the strategic and frugal use of only two human annotators could enable the efficient training of classifiers with reasonably good performance. This study does not aim to provide generalizability of the results. Rather, we propose that the annotation strategies arising from this study be considered by our readers only if such strategies are fit for one’s specific research purposes.

Multilingual corpora are difficult to compile and a classroom setting adds pedagogy to the mix of factors which make this data so rich and problematic to classify. In this paper, we set out methodological considerations of using automated speech recognition to build a corpus of teacher speech in an Indonesian language classroom. Our preliminary results (64% word error rate) suggest these tools have the potential to speed data collection in this context. We provide practical examples of our data structure, details of our piloted computer-assisted processes, and fine-grained error analysis. Our study is informed and directed by genuine research questions and discussion in both the education and computational linguistics fields. We highlight some of the benefits and risks of using these emerging technologies to analyze the complex work of language teachers and in education more generally.

Speech visualisations are known to help language learners to acquire correct pronunciation and promote a better study experience. We present a two-step approach based on two established techniques to display tongue tip movements of an acoustic speech signal on a vowel space plot. First we use Energy Entropy Ratio to extract vowels; and then we apply Linear Predictive Coding root method to estimate Formant 1 and Formant 2. We invited and collected acoustic data from one Modern Standard Arabic (MSA) lecture and four MSA students. Our proof of concept was able to reflect differences between the tongue tip movements in a native MSA speaker to those of a MSA language learner. This paper addresses principle methods for generating features that reflect bio-physiological features of speech and thus, facilitates an approach that can be generally adapted to languages other than MSA.

In neural semantic parsing, sentences are mapped to meaning representations using encoder-decoder frameworks. In this paper, we propose to apply the Transformer architecture, instead of recurrent neural networks, to this task. Experiments in two data sets from different domains and with different levels of difficulty show that our model achieved better results than strong baselines in certain settings and competitive results across all our experiments.

This paper describes the development of a verbal morphological parser for an under-resourced Papuan language, Nen. Nen verbal morphology is particularly complex, with a transitive verb taking up to 1,740 unique features. The structural properties exhibited by Nen verbs raises interesting choices for analysis. Here we compare two possible methods of analysis: ‘Chunking’ and decomposition. ‘Chunking’ refers to the concept of collating morphological segments into one, whereas the decomposition model follows a more classical linguistic approach. Both models are built using the Finite-State Transducer toolkit foma. The resultant architecture shows differences in size and structural clarity. While the ‘Chunking’ model is under half the size of the full de-composed counterpart, the decomposition displays higher structural order. In this paper, we describe the challenges encountered when modelling a language exhibiting distributed exponence and present the first morphological analyser for Nen, with an overall accuracy of 80.3%.

Over 60% of Australian PhD graduates land their first job after graduation outside academia, but this job market remains largely hidden to these job seekers. Employers’ low awareness and interest in attracting PhD graduates means that the term “PhD” is rarely used as a keyword in job advertisements; 80% of companies looking to employ similar researchers do not specifically ask for a PhD qualification. As a result, typing in “PhD” to a job search engine tends to return mostly academic jobs. We set out to make the market for advanced research skills more visible to job seekers. In this paper, we present PostAc, an online platform of authentic job postings that helps PhD graduates sharpen their career thinking. The platform is underpinned by research on the key factors that identify what an employer is looking for when they want to hire a highly skilled researcher. Its ranking model leverages the free-form text embedded in the job description to quantify the most sought-after PhD skills and educate information seekers about the Australian job-market appetite for PhD skills. The platform makes visible the geographic location, industry sector, job title, working hours, continuity, and wage of the research intensive jobs. This is the first data-driven exploration in this field. Both empirical results and online platform will be presented in this paper.

This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media — more precisely, Twitter — with its aim to predict the most likely associated emoji of a tweet. Our solution for this text classification problem explores the idea of transfer learning for adapting the classifier based on users’ tweeting history. Our experiments show that our user-adaption method improves classification results by more than 6 per cent on the macro-averaged F1. Thus, our paper provides evidence for the rationality of enriching the original corpus longitudinally with user behaviors and transferring the lessons learned from corresponding users to specific instances.

Verbal communication — and pronunciation as its part — is a core skill that can be developed through guided learning. An artificial intelligence system can take a role in these guided learning approaches as an enabler of an application for pronunciation learning with a recommender system to guide language learners through exercises and feedback system to correct their pronunciation. In this paper, we report on a user study on language learners’ perceived usefulness of the application. 16 international students who spoke non-native English and lived in Australia participated. 13 of them said they need to improve their pronunciation skills in English because of their foreign accent. The feedback system with features for pronunciation scoring, speech replay, and giving a pronunciation example was deemed essential by most of the respondents. In contrast, a clear dichotomy between the recommender system perceived as useful or useless existed; the system had features to prompt new common words or old poorly-scored words. These results can be used to target research and development from information retrieval and reinforcement learning for better and better recommendations to speech recognition and speech analytics for accent acquisition.