ATILA'17

Also cake.

Imagination improves Multimodal Translation

Desmond Elliot, Ákos Kádár

We decompose multi-modal translation into two sub-tasks: learning to translate and learning visually grounded representations. In a multi-task learning framework, translations are learned in an attention-based encoder-decoder, and grounded representations are learned through image representation prediction. Our approach improves translation performance compared to the state of the art on the Multi30K dataset. Furthermore, it is equally effective if we train the image prediction task on the external MS COCO dataset, and we find improvements if we train the translation model on the external News Commentary parallel text.

Simulating hearing loss in neural networks: does pre-training on intact speech boost performance on degraded input?

Robert Grimm , Michèle Pettinato

We present results from speech recognition experiments designed to mimic conditions in human listeners with cochlear implants (CIs) -- devices which deliver sound to the brain in individuals with hearing loss. Postlingually deaf (PD) individuals, who receive CIs after a period of normal hearing, typically perform better on hearing-related tasks than congenitally deaf (CD) individuals, who are born deaf and are implanted at an early age. We consider two possible reasons for this: CD individuals might perform worse than PD individuals because they develop hearing after a period of auditory deprivation. Then, after implantation with a CI, the brain may have lost the plasticity necessary for full recovery. Alternatively, CD individuals might not have reduced listening capabilities. Rather, the signals delivered by CIs might not be rich enough for CD individuals to differentiate the fine-grained speech structure that PD listeners learn to discriminate during the period of normal hearing prior to implantation. To evaluate these possibilities, we train neural networks on (a) normal speech and (b) vo-coded speech that is modified to simulate the input received by people with CIs. We then compare the performance of two networks: a CD network, which is only trained on vocoded speech; and a PD network, which is pre-trained on normal speech before the training data are vocoded. Crucially, and in contrast to CD individuals, the CD network has the same learning capacity as the PD network. Thus, if pre-training the PD network improves performance on vocoded speech relative to the CD network, this would constitute evidence that exposure to intact speech can boost performance on subsequently degraded input. This would suggest that the difference in performance between PD and CD individuals is not due to reduced learning capabilities in CD individuals, but instead has to do with the impoverished signals delivered by CIs.

Predicting life expectancy with a long short-term memory to aid in early identification of the palliative phase

Merijn Beeksma , Suzan Verberne

Life expectancy is a leading indicator when making decisions about end-of-life care, but good prognostication is notoriously challenging. Being overly optimistic about life expectancy, as doctors tend to be, greatly impedes the early identification of palliative patients and thereby delays appropriate care in the final phase of life. This research aimed to explore the feasibility of automatically predicting life expectancy based on electronic medical records, with the aid of machine learning and natural language processing techniques.
We trained a neural network (long short-term memory) with 1107 medical records, and validated the model with 127 medical records. Using identical evaluation criteria as were used to evaluate doctors'€™ performance, our baseline model reached a level of accuracy similar to human accuracy. The inclusion of clinical narrative was enabled and optimized with the use of natural language processing techniques such as domain-specific spelling correction. The inclusion of keyword features improved the prediction accuracy with 9%, compared to both our baseline model and to the golden standard of human evaluation. Overall, we have shown that our approach for automatic prognostication is feasible and delivers promising results.

Clinical Machine Comprehension with Case Reports

Simon Šuster , Walter Daelemans

Machine reading comprehension is a form of question answering in which the correct answer to a query can be found by reading a document. In this talk, we present our work in machine comprehension by focusing on the creation of a large resource that allows us to study reading comprehension in the clinical domain. We analyze the performance of both human and machine readers on a gap-filling task, and examine what skills are required to understand the supporting text and to provide the right answers. We also position our dataset and task formulation with respect to other popular reading comprehension works.

Organic soup, worstenbrood, pistolets with organic cheese and/or ham with salad and dressing, coffee, tea, orange juice and organic milk.

Social media and social class: Correlations between adolescents' online writing and their social background

Lisa Hilte , Walter Daelemans, Reinhild Vandekerckhove

In this study, we analyze correlations between Flemish adolescents' language use on social media and their social class. The participants' social class has been operationalized as a cluster of three subvariables (representing different aspects of class, such as cultural, financial, etc.). These three variables are the adolescents' educational track, their home language and the profession of their parents.

All three variables significantly influence the teenagers' 'non-standard' language use on social media. (In the present case study, the category of non-standard features covers a wide range of deviations from formal Dutch standard writing, such as dialect words, emoticons, chatspeak abbreviations, etc.). A gradual increase in non-standardness can be found for educational track (more non-standard features in more practical types of secondary education), for profession of the parents (more non-standard features when parents have 'lower' class professions) and for home language (more non-standard features when a language other than Dutch is spoken at home). After clustering the subvariables to create two groups of teenagers holding extreme positions on the social continuum, even more distinct linguistic profiles emerge.

Additional analyses reveal the complexity of adolescents' social class: interactions with both age and gender can be found (i.e. social class does not have the same impact on girls versus boys, or on younger versus older teenagers). When we expand the research to non-prototypical groups of teenagers (i.e. teenagers which do not clearly belong to higher or lower class, but have a more 'hybrid' profile, and are somewhere in-between on the social continuum), deviant linguistic patterns emerge, which seems to suggest that additional factors such as social mobility, ambition, and the structure of peer networks need to be taken into account.

Finally, we will discuss some ideas and challenges for future research.

Simple Queries as Distant Labels for Detecting Gender on Twitter

Chris Emmery , Grzegorz Chrupała, Walter Daelemans

The majority of research on extracting missing user attributes from social media profiles use costly hand-annotated labels for supervised learning. Distantly supervised methods exist, although these generally rely on knowledge gathered using external sources. This paper demonstrates the effectiveness of gathering distant labels for self-reported gender on Twitter using simple queries. We confirm the reliability of this query heuristic by comparing with manual annotation. Moreover, using these labels for distant supervision, we demonstrate competitive model performance on the same data as models trained on manual annotations. As such, we offer a cheap, extensible, and fast alternative that can be employed beyond the task of gender classification. Data and code are available open-source here.

FoLiA: The saga continues

Maarten van Gompel , Ko van der Sloot

We present an update on FoLiA, the Format for Linguistic Annotation and the rich infrastructure surrounding it. We will give a brief shout-out of new features in FoLiA v1.4 (august 2016) and FoLiA v1.5 (released october 2017) and stress the need for well-developed data formats in our field. We'll highlight ties to linked open data, richer support for metadata, and newly updated tools such as a ReStructuredText to FoLiA converter. We will have a practical hands-on focus and point users to the Python library, the C++ library, and the various FoLiA tools and utilities.

FLAT: A web-based linguistic annotation environment

Maarten van Gompel

Featured as a demo at last year's ATILA, we again present FLAT, a web-based linguistic annotation environment based on FoLiA, and show what has been improved in the meantime.

Frog & ucto: The next leap

Maarten van Gompel , Ko van der Sloot, Antal van den Bosch, Iris Hendrickx

We present what's new with Frog and ucto. Frog is a rich NLP-suite, an integration of memory-based natural language processing (NLP) modules developed for Dutch. It features native FoLiA support, comes with a Python binding, and is currently funded by CLARIAH. Ucto is a multilingual rule-based tokeniser, also embedded into Frog.

CLAM: Turning command-line tools into RESTful webservices since 2011

Maarten van Gompel

CLAM has been in use for quite some years to make command-line tools available over the web as RESTful webservices with an end-user web-application interfaces. We briefly show what it does and point users to the collection of webservices hosted at Radboud University Nijmegen.

LaMachine: A flexible software distribution of inter-dependent NLP software

Maarten van Gompel

We present LaMachine, a software distribution we use to release and distribute all of our NLP software. It comes in three flavours for flexibility: a Virtual Machine, a Docker container and a local installation script. We invite third parties to join in if interested.

TICCLAT: Text-Induced Corpus Correction and Lexical Assessment Tool

Martin Reynaert , Emmanuel Keuleers and Ko van der Sloot

We present the CLARIAH / eScience Center TICCLAT project due to start January 2018. We will extend TICCL's correction capabilities with classification facilities based on specific data collected from the full diachronic Dutch Nederlab corpus: word statistics, document and time references and linguistic annotations, i.e. Part-of Speech and Named-Entity labels. These data will complement a solid, renewed basis composed of the available validated lexicons and name lists for Dutch. In this, TICCL as a post-correction tool will be transformed into TICCLAT, a lexical assessment tool capable of delivering not only correction candidates, but also e.g. more accurately dated diachronic Dutch word forms, more securely classified person and place names. To achieve this on scale, the TICCLAT project will seek a successful merger of TICCL's anagram hashing with bit-vectorization techniques. TICCLAT's capabilities will also be evaluated in comparison to human performance by an expert psycholinguist.

Unsupervised dense patient representations from unstructured clinical data with interpretable classification decisions

Madhumita Sushil , Simon Suster, Kim Luyckx, Walter Daelemans

We learn unsupervised dense patient representations from unstructured clinical data using a stacked denoising autoencoder and a paragraph vector model. We evaluate the representations by using them as features for multiple independent tasks, and compare the performance with those of sparse representations. We explore the best encoded features within the representations, and extract the most significant features when the pretrained representations are used as the input to the classifiers.

Update on TraMOOC: implicit MT evaluation

Iris Hendrickx , Maarten van Gompel and Antal van den Bosch

We present the latest outcomes on our experiments on Implicit evaluation of Machine Translation.

Challenges and strategies for medical NER in Dutch

Charlotte Hansart

During this ligthning talk, I 'll expose the challenges faced by a company focused on medical NER in Dutch. Then I'll expose the strategy we adopted and the leads we investigate to improve the performances of our system.

Patterns of keystrokes in writing

Rianne Conijn, Menno van Zaanen

We will present how patterns can be identified in keystroke data obtained during different writing tasks, and how this information can be used to predict the writing task.

Grammar-Based and Lexicon-Based Techniques to Extract Personality Traits from Text

Maira B. Carvalho , Max Louwerse

Language provides an important source of information to predict human personality. However, most studies that have predicted personality traits using computational linguistic methods have focused on lexicon-based information. We investigate to what extent the performance of lexicon-based and grammar-based methods compare when predicting personality traits. We analyzed a corpus of student essays and their personality traits using two lexicon-based approaches, one top-down (Linguistic Inquiry and Word Count (LIWC)), one bottom-up (topic models) and one grammar-driven approach (Biber model), as well as combinations of these models. Results showed that the performance of the models and their combinations demonstrated similar performance, showing that lexicon-based top-down models and bottom-up models do not differ, and neither do lexicon-based models and grammar-based models. Moreover, combination of models did not improve performance. These findings suggest that predicting personality traits from text remains difficult, but that the performance from lexicon-based and grammar-based models are on par.

SemEval-2018 Task 3 - Automatic irony detection in English tweets

Els Lefever, Véronique Hoste

The development of the social web has stimulated creative and figurative language use like irony. This frequent use of irony on social media has important implications for natural language processing tasks, which struggle to maintain high performance when applied to ironic text (Liu, 2012; Maynard and Greenwood, 2014; Ghosh and Veale, 2016). The goal of the SemEval-2018 Task 3 is to provide the research community with a manually annotated irony dataset and to incite this community to develop state-of-the-art irony classification systems. The dataset provides binary and fine-grained class labels, allowing to develop binary (subtask A) and multi-class (subtask B) irony classifiers. Participants of the task are invited to submit their systems until 22 (subtask A) and 29 (subtask B) January 2018.

Flow.ai: the Conversational AI toolkit

Sander Wubben

Flow.ai is a complete suite for conversational AI design. It provides an intuitive drag and drop interface with integrated NLP. Flow.ai can be used to create, train and host chatbots and smart assistants and deploy them on a variety of channels, for example web chat, Facebook Messenger, Amazon Alexa or Rocket.Chat. Sign up at https://flow.ai!

Het Wapen Van Tilburg

Our dinner and social activity will held at Het Wapen Van Tilburg, just down the road of the main location (see the map). On the menu will be the following:

Starter

Several small starters creatively presented.

Main

Choice of: Steak with pepper sauce, Vegetable tempura with rice, or Cod tail with beurre blanc.

Dessert

Choice of: Cheese platter, or a Sweet dessert.

Drinks

Everyone has up to six drinks (beer / wine / non-alcoholic) at their disposal for the entire evening (including social activity). If you're planning on drinking less, treat someone thirsty. :)