The one-sided focus on English in previous studies of gender bias in NLP misses out on opportunities in other languages: English challenge datasets such as GAP and Wino-Gender highlight model preferences that are "hallucinatory", e.g., disambiguating genderambiguous occurrences of 'doctor' as male doctors.We show that for languages with type B reflexivization, e.g., Swedish and Russian, we can construct multi-task challenge datasets for detecting gender bias that lead to unambiguously wrong model predictions: In these languages, the direct translation of 'the doctor removed his mask' is not ambiguous between a coreferential reading and a disjoint reading.Instead, the coreferential reading requires a non-gendered pronoun, and the gendered, possessive pronouns are anti-reflexive.We present a multilingual, multi-task challenge dataset, which spans four languages and four NLP tasks and focuses only on this phenomenon.We find evidence for gender bias across all task-language combinations and correlate model bias with national labor market statistics.

In this thesis, I focus on language independent methods of improving utterance understanding and response generation and attempt to tackle some of the issues surrounding current systems. The aim is to create a unified approach to dialogue generation inspired by developments in both goal oriented and open ended dialogue systems. The main contributions in this thesis are: 1) Introducing hybrid approaches to dialogue generation using retrieval and encoder-decoder architectures to produce fluent but precise utterances in dialogues, 2) Proposing supervised, semi-supervised and Reinforcement Learning methods for domain adaptation in goal oriented dialogue and 3) Introducing models that can adapt cross lingually.

Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez, Daniel Herschcovich, Chen Qiu, Anders Sandholm, Michael Ringaard, Anders Søgaard. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Readability assessment can improve the quality of assisting technologies aimed at language learners. Eye-tracking data has been used for both inducing and evaluating general-purpose NLP/AI models, and below we show that unsurprisingly, gaze data from language learners can also improve multi-task readability assessment models. This is unsurprising, since the gaze data records the reading difficulties ofthe learners. Unfortunately, eye-tracking data from language learners is often much harder to obtain than eye-tracking data from native speakers. We therefore compare the performance of deep learning readability models that use nativespeaker eye movement data to models using data from language learners. Somewhat surprisingly, we observe no significant drop in performance when replacing learners with natives, making approaches that rely on native speaker gaze information, more scalable. In other words, our finding is that language learner difficulties can be efficiently estimated from native speakers, which suggests that, more generally, readily available gaze data can be used to improve educational NLP/AI models targeted towards language learners.

The best systems at the SemEval-16 and SemEval-17 community question answering shared tasks -a task that amounts to question relevancy ranking -involve complex pipelines and manual feature engineering.Despite this, many of these still fail at beating the IR baseline, i.e., the rankings provided by Google's search engine.We present a strong baseline for question relevancy ranking by training a simple multi-task feed forward network on a bag of 14 distance measures for the input question pair.This baseline model, which is fast to train and uses only language-independent features, outperforms the best shared task systems on the task of retrieving relevant previously asked questions.

Maria Barrett, Ana Valeria González-Garduño, Lea Frermann, Anders Søgaard. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.