Publications

Abstract

Metaphor allows us to think and talk about one thing in terms of another, ratcheting up our cognitive and expressive capacity. It gives us concrete terms for abstract phenomena, for example, ideas become things we can grasp or let go of. Perceptual experience—characterised as physical and relatively concrete—should be an ideal source domain in metaphor, and a less likely target. But is this the case across diverse languages? And are some sensory modalities perhaps more concrete than others? This volume presents critical new data on perception metaphors from over 40 languages, including many which are under-studied. Aside from the wealth of data from diverse languages—modern and historical; spoken and signed—a variety of methods (e.g., natural language corpora, experimental) and theoretical approaches are brought together. This collection highlights how perception metaphor can offer both a bedrock of common experience and a source of continuing innovation in human communication

Abstract

One of the most pressing issues in dis-
continuous constituency transition-based
parsing is that the relevant information for
parsing decisions could be located in any
part of the stack or the buffer. In this pa-
per, we propose a solution to this prob-
lem by replacing the structured percep-
tron model with a recursive neural model
that computes a global representation of
the configuration, therefore allowing even
the most remote parts of the configura-
tion to influence the parsing decisions. We
also provide a detailed analysis of how
this representation should be built out of
sub-representations of its core elements
(words, trees and stack). Additionally, we
investigate how different types of swap or-
acles influence the results. Our model is
the first neural discontinuous constituency
parser, and it outperforms all the previ-
ously published models on three out of
four datasets while on the fourth it obtains
second place by a tiny difference.

Abstract

Motivated form-meaning mappings are pervasive in sign languages, and iconicity has recently been shown to facilitate sign learning from early on. This study investigated the role of iconicity for language acquisition in Turkish Sign Language (TID). Participants were 43 signing children (aged 10 to 45 months) of deaf parents. Sign production ability was recorded using the adapted version of MacArthur Bates Communicative Developmental Inventory (CDI) consisting of 500 items for TID. Iconicity and familiarity ratings for a subset of 104 signs were available. Our results revealed that the iconicity of a sign was positively correlated with the percentage of children producing a sign and that iconicity significantly predicted the percentage of children producing a sign, independent of familiarity or phonological complexity. Our results are consistent with previous findings on sign language acquisition and provide further support for the facilitating effect of iconic form-meaning mappings in sign learning.

Abstract

Sequences of reaction times (RT) produced by participants in an experiment are not only influenced by the stimuli, but by many other factors as well, including fatigue, attention, experience, IQ, handedness, etc. These confounding factors result in longterm effects (such as a participant’s overall reaction capability) and in short- and medium-time fluctuations in RTs (often referred to as ‘local speed effects’). Because stimuli are usually presented in a random sequence different for each participant, local speed effects affect the underlying ‘true’ RTs of specific trials in different ways across participants. To be able to focus statistical analysis on the effects of the cognitive process under study, it is necessary to reduce the effect of confounding factors as much as possible. In this paper we propose and compare techniques and criteria for doing so, with focus on reducing (‘filtering’) the local speed effects. We show that filtering matters substantially for the significance analyses of predictors in linear mixed effect regression models. The performance of filtering is assessed by the average between-participant correlation between filtered RT sequences and by Akaike’s Information Criterion, an important measure of the goodness-of-fit of linear mixed effect regression models.

Abstract

The neural processing of speech leads to specific patterns in the brain which can be measured as, e.g., EEG signals. When properly aligned with the speech input and averaged over many tokens, the Event Related Potential (ERP) signal is able to differentiate specific contrasts between speech signals. Well-known effects relate to the difference between expected and unexpected words, in particular in the N400, while effects in N100 and P200 are related to attention and acoustic onset effects. Most EEG studies deal with the amplitude of EEG signals over time, sidestepping the effect of phase and phase synchronization. This paper investigates the relation between phase in the EEG signals measured in an auditory lexical decision task by Dutch participants listening to full and reduced English word forms. We show that phase synchronization takes place across stimulus conditions, and that the so-called circular variance is narrowly related to the type of contrast between stimuli.

Abstract

This paper combines two different approaches to modeling reaction time data from lexical decision experiments, viz. a dataoriented statistical analysis by means of a linear mixed effects model, and a process-oriented computational model of human speech comprehension. The linear mixed effect model is implemented by lmer in R. As computational model we apply DIANA, an end-to-end computational model which aims at modeling the cognitive processes underlying speech comprehension. DIANA takes as input the speech signal, and provides as output the orthographic transcription of the stimulus, a word/non-word judgment and the associated reaction time. Previous studies have shown that DIANA shows good results for large-scale lexical decision experiments in Dutch and North-American English. We investigate whether predictors that appear significant in an lmer analysis and processes implemented in DIANA can be related and inform both approaches. Predictors such as ‘previous reaction time’ can be related to a process description; other predictors, such as ‘lexical neighborhood’ are hard-coded in lmer and emergent in DIANA. The analysis focuses on the interaction between subject variables and task variables in lmer, and the ways in which these interactions can be implemented in DIANA.

Abstract

The recent advent of deep learning techniques in speech tech-nology and in particular in automatic speech recognition hasyielded substantial performance improvements. This suggeststhat deep neural networks (DNNs) are able to capture structurein speech data that older methods for acoustic modeling, suchas Gaussian Mixture Models and shallow neural networks failto uncover. In image recognition it is possible to link repre-sentations on the first couple of layers in DNNs to structuralproperties of images, and to representations on early layers inthe visual cortex. This raises the question whether it is possi-ble to accomplish a similar feat with representations on DNNlayers when processing speech input. In this paper we presentthree different experiments in which we attempt to untanglehow DNNs encode speech signals, and to relate these repre-sentations to phonetic knowledge, with the aim to advance con-ventional phonetic concepts and to choose the topology of aDNNs more efficiently. Two experiments investigate represen-tations formed by auto-encoders. A third experiment investi-gates representations on convolutional layers that treat speechspectrograms as if they were images. The results lay the basisfor future experiments with recursive networks.

Abstract

In spontaneous conversations, words are often produced in reduced form compared to formal careful speech. In English, for instance, ’probably’ may be pronounced as ’poly’ and ’police’ as ’plice’. Reduced forms are very common, and native listeners usually do not have any problems with interpreting these reduced forms in context. Non-native listeners, however, have great difficulties in comprehending reduced forms. In order to investigate the problems in comprehension that non-native listeners experience, a dictation experiment was conducted in which sentences were presented auditorily to non-natives either in full (unreduced) or reduced form. The types of errors made by the L2 listeners reveal aspects of the cognitive processes underlying this dictation task. In addition, we compare the errors made by these human participants with the type of word errors made by DIANA, a recently developed computational model of word comprehension.

Abstract

This paper investigates the processes in comprehending spoken noun-noun compounds, using data from the BALDEY database. BALDEY contains lexicality judgments and reaction times (RTs) for Dutch stimuli for which also linguistic information is included. Two different approaches are combined. The first is based on regression by Dynamic Survival Analysis, which models decisions and RTs as a consequence of the fact that a cumulative density function exceeds some threshold. The parameters of that function are estimated from the observed RT data. The second approach is based on DIANA, a process-oriented computational model of human word comprehension, which simulates the comprehension process with the acoustic stimulus as input. DIANA gives the identity and the number of the word candidates that are activated at each 10 ms time step.

Both approaches show how the processes involved in comprehending compounds change during a stimulus. Survival Analysis shows that the impact of word duration varies during the course of a stimulus. The density of word and non-word hypotheses in DIANA shows a corresponding pattern with different regimes. We show how the approaches complement each other, and discuss additional ways in which data and process models can be combined.

Abstract

In everyday life, people see, describe and remember motion events. We tested whether the type of motion event information (path or manner) encoded in speech and gesture predicts which information is remembered and if this varies across speakers of typologically different languages. We focus on intransitive motion events (e.g., a woman running to a tree) that are described differently in speech and co-speech gesture across languages, based on how these languages typologically encode manner and path information (Kita & Özyürek, 2003; Talmy, 1985). Speakers of Dutch (n = 19) and Turkish (n = 22) watched and described motion events. With a surprise (i.e. unexpected) recognition memory task, memory for manner and path components of these events was measured. Neither Dutch nor Turkish speakers’ memory for manner went above chance levels. However, we found a positive relation between path speech and path change detection: participants who described the path during encoding were more accurate at detecting changes to the path of an event during the memory task. In addition, the relation between path speech and path memory changed with native language: for Dutch speakers encoding path in speech was related to improved path memory, but for Turkish speakers no such relation existed. For both languages, co-speech gesture did not predict memory speakers. We discuss the implications of these findings for our understanding of the relations between speech, gesture, type of encoding in language and memory.

Abstract

We estimate lexical Concreteness for millions of words across 77 languages. Using a simple regression framework, we combine vector-based models of lexical semantics with experimental norms of Concreteness in English and Dutch. By applying techniques to align vector-based semantics across distinct languages, we compute and release Concreteness estimates at scale in numerous languages for which experimental norms are not currently available. This paper lays out the technique and its efficacy. Although this is a difficult dataset to evaluate immediately, Concreteness estimates computed from English correlate with Dutch experimental norms at $\rho$ = .75 in the vocabulary at large, increasing to $\rho$ = .8 among Nouns. Our predictions also recapitulate attested relationships with word frequency. The approach we describe can be readily applied to numerous lexical measures beyond Concreteness

Abstract

Do all languages convey semantic knowledge in the same way? If language simply mirrors the structure of the world, the answer should be a qualified “yes”. If, however, languages impose structure as much as reflecting it, then even ostensibly the “same” word in different languages may mean quite different things. We provide a first pass at a large-scale quantification of cross-linguistic semantic alignment of approximately 1000 meanings in 55 languages. We find that the translation equivalents in some domains (e.g., Time, Quantity, and Kinship) exhibit high alignment across languages while the structure of other domains (e.g., Politics, Food, Emotions, and Animals) exhibits substantial cross-linguistic variability. Our measure of semantic alignment correlates with known phylogenetic distances between languages: more phylogenetically distant languages have less semantic alignment. We also find semantic alignment to correlate with cultural distances between societies speaking the languages, suggesting a rich co-adaptation of language and culture even in domains of experience that appear most constrained by the natural world

Abstract

In situated communication, reference to an entity in the shared visual context can be established using eitheranexpression that conveys precise (minimally specified) or redundant (over-specified) information. There is, however, along-lasting debate in psycholinguistics concerningwhether the latter hinders referential processing. We present evidence from an eyetrackingexperiment recordingfixations as well asthe Index of Cognitive Activity –a novel measure of cognitive workload –supporting the view that over-specifications facilitate processing. We further present originalevidence that, above and beyond the effect of specificity,referring expressions thatuniformly reduce referential entropyalso benefitprocessing

Abstract

This paper describes the development of a CLARIN-compatible repository solution that fulfils
both the long-term preservation requirements as well as the current day discoverability and usability
needs of an online data repository of language resources. The widely used Fedora Commons
open source repository framework, combined with the Islandora discovery layer, forms
the basis of the solution. On top of this existing solution, additional modules and tools are developed
to make it suitable for the types of data and metadata that are used by the participating
partners.

Abstract

Even advanced Spanish speakers of second language English tend to confuse the pronouns ‘he’ and ‘she’, often without even noticing their mistake (Lahoz, 1991). A study by AntónMéndez (2010) has indicated that a possible reason for this error is the fact that Spanish is a pro-drop language. In order to test this hypothesis, we used an extension of Dual-path (Chang, 2002), a computational cognitive model of sentence production, to simulate two models of bilingual speech production of second language English. One model had Spanish (ES) as a native language, whereas the other learned a Spanish-like language that used the pronoun at all times (non-pro-drop Spanish, NPD_ES). When tested on L2 English sentences, the bilingual pro-drop Spanish model produced significantly more gender pronoun errors, confirming that pronoun dropping could indeed be responsible for the gender confusion in natural language use as well.

Abstract

The task of automatic playlist continuation is generating a list of recommended tracks that can be added to an existing playlist. By suggesting appropriate tracks, i. e., songs to add to a playlist, a recommender system can increase the user engagement by making playlist creation easier, as well as extending listening beyond the end of current playlist. The ACM Recommender Systems Challenge 2018 focuses on such task. Spotify released a dataset of playlists, which includes a large number of playlists and associated track listings. Given a set of playlists from which a number of tracks have been withheld, the goal is predicting the missing tracks in those playlists. We participated in the challenge as the team Unconscious Bias and, in this paper, we present our approach. We extend adversarial autoencoders to the problem of automatic playlist continuation. We show how multiple input modalities, such as the playlist titles as well as track titles, artists and albums, can be incorporated in the playlist continuation task.

Abstract

This corpus study investigates how children figure out that functional modals
like must can express various flavors of modality. We examine how modality is
expressed in speech to and by children, and find that the way speakers use
modals may obscure their polysemy. Yet, children eventually figure it out. Our
results suggest that some do before age 3. We show that while root and
epistemic flavors are not equally well-represented in the input, there are robust
correlations between flavor and aspect, which learners could exploit to discover
modal polysemy.

Abstract

Before infants become mature speakers of their native language, they must acquire a robust word-recognition system which allows them to strike the balance between allowing some variation (mood, voice, accent) and recognizing variability that potentially changes meaning (e.g. cat vs hat). The current meta-analysis quantifies how the latter, termed mispronunciation sensitivity, changes over infants’ first three years, testing competing predictions of mainstream language acquisition theories. Our results show that infants were sensitive to mispronunciations, but accepted them as labels for target objects. Interestingly, and in contrast to predictions of mainstream theories, mispronunciation sensitivity was not modulated by infant age, suggesting that a sufficiently flexible understanding of native language phonology is in place at a young age.

Abstract

This study concerns individual differences in speech imitation ability and the role that lexical representations play in imitation. We examined 1) whether imitation of sounds in an unfamiliar language (L0) is related to imitation of sounds in an unfamiliar
non-native accent in the speaker’s native language (L1) and 2) whether it is easier or harder to imitate speech when you know the words to be imitated. Fifty-nine native Dutch speakers imitated words with target vowels in Basque (/a/ and /e/) and Greekaccented
Dutch (/i/ and /u/). Spectral and durational
analyses of the target vowels revealed no relationship between the success of L0 and L1 imitation and no difference in performance between tasks (i.e., L1
imitation was neither aided nor blocked by lexical knowledge about the correct pronunciation). The results suggest instead that the relationship of the vowels to native phonological categories plays a bigger role in imitation

Abstract

Language resources are valuable assets, both for institutions and researchers. To safeguard these resources requirements for repository systems and data management have been specified by various branch organizations, e.g., CLARIN and the Data Seal of Approval. This paper describes these and some additional ones posed by the authors’ home institutions. And it shows how they are met by FLAT, to provide a new home for language resources. The basis of FLAT is formed by the Fedora Commons repository system. This repository system can meet many of the requirements out-of-the box, but still additional configuration and some development work is needed to meet the remaining ones, e.g., to add support for Handles and Component Metadata. This paper describes design decisions taken in the construction of FLAT’s system architecture via a mix-and-match strategy, with a preference for the reuse of existing solutions. FLAT is developed and used by the a Institute and The Language Archive, but is also freely available for anyone in need of a CLARIN-compliant repository for their language resources.

Abstract

Previous research on basic-level object categories shows there is cross-cultural variation in basic-level concepts, arguing against the idea that the basic level reflects an objective reality. In this paper, I extend the investigation to the domain of events. More specifically, I present a case study of verbs of ingestion in Maniq illustrating a highly specific categorization of ingestion events at the basic level. A detailed analysis of these verbs reveals they tap into culturally salient notions. Yet, cultural salience alone cannot explain specificity of basic-level verbs, since ingestion is a domain of universal human experience. Further analysis reveals, however, that another key factor is the language itself. Maniq’s preference for encoding specific meaning in basic-level verbs is not a peculiarity of one domain, but a recurrent characteristic of its verb lexicon, pointing to the significant role of the language system in the structure of event concepts

Abstract

It is unknown whether modality affects the efficiency with which humans learn novel word forms and their meanings, with previous studies reporting both written and auditory advantages. The current study implements controls whose absence in previous work likely offers explanation for such contradictory findings. In two novel word learning experiments, participants were trained and tested on pseudoword - novel object pairs, with controls on: modality of test, modality of meaning, duration of exposure and transparency of word form. In both experiments word forms were presented in either their written or spoken form, each paired with a pictorial meaning (novel object). Following a 20-minute filler task, participants were tested on their ability to identify the picture-word form pairs on which they were trained. A between subjects design generated four participant groups per experiment 1) written training, written test; 2) written training, spoken test; 3) spoken training, written test; 4) spoken training, spoken test. In Experiment 1 the written stimulus was presented for a time period equal to the duration of the spoken form. Results showed that when the duration of exposure was equal, participants displayed a written training benefit. Given words can be read faster than the time taken for the spoken form to unfold, in Experiment 2 the written form was presented for 300 ms, sufficient time to read the word yet 65% shorter than the duration of the spoken form. No modality effect was observed under these conditions, when exposure to the word form was equivalent. These results demonstrate, at least for proficient readers, that when exposure to the word form is controlled across modalities the efficiency with which word form-meaning associations are learnt does not differ. Our results therefore suggest that, although we typically begin as aural-only word learners, we ultimately converge on developing learning mechanisms that learn equally efficiently from both written and spoken materials.

Abstract

Young Infants are prolific word learners even though they are facing the challenge of referential uncertainty (Quine, 1960). Many laboratory studies have shown that infants are skilled at inferring correct referents of words from ambiguous contexts (Swingley, 2009). However, little is known regarding how they visually attend to and select the target object among many other objects in view when parents name it during everyday interactions. By investigating the looking pattern of 12-month-old infants using naturalistic first-person images with varying degrees of referential ambiguity, we found that infants’ attention is selective and they only select a small subset of objects to attend to at each learning instance despite the complexity of the data in the real world. This work allows us to better understand how perceptual properties of objects in infants’ view influence their visual attention, which is also related to how they select candidate objects to build word-object mappings.