Afra Alishahi


2020

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Learning to Understand Child-directed and Adult-directed Speech
Lieke Gelderloos | Grzegorz Chrupała | Afra Alishahi
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Speech directed to children differs from adult-directed speech in linguistic aspects such as repetition, word choice, and sentence length, as well as in aspects of the speech signal itself, such as prosodic and phonemic variation. Human language acquisition research indicates that child-directed speech helps language learners. This study explores the effect of child-directed speech when learning to extract semantic information from speech directly. We compare the task performance of models trained on adult-directed speech (ADS) and child-directed speech (CDS). We find indications that CDS helps in the initial stages of learning, but eventually, models trained on ADS reach comparable task performance, and generalize better. The results suggest that this is at least partially due to linguistic rather than acoustic properties of the two registers, as we see the same pattern when looking at models trained on acoustically comparable synthetic speech.

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Analyzing analytical methods: The case of phonology in neural models of spoken language
Grzegorz Chrupała | Bertrand Higy | Afra Alishahi
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Given the fast development of analysis techniques for NLP and speech processing systems, few systematic studies have been conducted to compare the strengths and weaknesses of each method. As a step in this direction we study the case of representations of phonology in neural network models of spoken language. We use two commonly applied analytical techniques, diagnostic classifiers and representational similarity analysis, to quantify to what extent neural activation patterns encode phonemes and phoneme sequences. We manipulate two factors that can affect the outcome of analysis. First, we investigate the role of learning by comparing neural activations extracted from trained versus randomly-initialized models. Second, we examine the temporal scope of the activations by probing both local activations corresponding to a few milliseconds of the speech signal, and global activations pooled over the whole utterance. We conclude that reporting analysis results with randomly initialized models is crucial, and that global-scope methods tend to yield more consistent and interpretable results and we recommend their use as a complement to local-scope diagnostic methods.

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Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
Afra Alishahi | Yonatan Belinkov | Grzegorz Chrupała | Dieuwke Hupkes | Yuval Pinter | Hassan Sajjad
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

2019

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Correlating Neural and Symbolic Representations of Language
Grzegorz Chrupała | Afra Alishahi
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Analysis methods which enable us to better understand the representations and functioning of neural models of language are increasingly needed as deep learning becomes the dominant approach in NLP. Here we present two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to directly quantify how strongly the information encoded in neural activation patterns corresponds to information represented by symbolic structures such as syntax trees. We first validate our methods on the case of a simple synthetic language for arithmetic expressions with clearly defined syntax and semantics, and show that they exhibit the expected pattern of results. We then our methods to correlate neural representations of English sentences with their constituency parse trees.

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On the difficulty of a distributional semantics of spoken language
Grzegorz Chrupała | Lieke Gelderloos | Ákos Kádár | Afra Alishahi
Proceedings of the Society for Computation in Linguistics (SCiL) 2019

2018

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Lessons Learned in Multilingual Grounded Language Learning
Ákos Kádár | Desmond Elliott | Marc-Alexandre Côté | Grzegorz Chrupała | Afra Alishahi
Proceedings of the 22nd Conference on Computational Natural Language Learning

Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language. Here, we investigate in detail which conditions affect the performance of this type of grounded language learning model. We show that multilingual training improves over bilingual training, and that low-resource languages benefit from training with higher-resource languages. We demonstrate that a multilingual model can be trained equally well on either translations or comparable sentence pairs, and that annotating the same set of images in multiple language enables further improvements via an additional caption-caption ranking objective.

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Revisiting the Hierarchical Multiscale LSTM
Ákos Kádár | Marc-Alexandre Côté | Grzegorz Chrupała | Afra Alishahi
Proceedings of the 27th International Conference on Computational Linguistics

Hierarchical Multiscale LSTM (Chung et. al., 2016) is a state-of-the-art language model that learns interpretable structure from character-level input. Such models can provide fertile ground for (cognitive) computational linguistics studies. However, the high complexity of the architecture, training and implementations might hinder its applicability. We provide a detailed reproduction and ablation study of the architecture, shedding light on some of the potential caveats of re-purposing complex deep-learning architectures. We further show that simplifying certain aspects of the architecture can in fact improve its performance. We also investigate the linguistic units (segments) learned by various levels of the model, and argue that their quality does not correlate with the overall performance of the model on language modeling.

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Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
Tal Linzen | Grzegorz Chrupała | Afra Alishahi
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

2017

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Representations of language in a model of visually grounded speech signal
Grzegorz Chrupała | Lieke Gelderloos | Afra Alishahi
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a visually grounded model of speech perception which projects spoken utterances and images to a joint semantic space. We use a multi-layer recurrent highway network to model the temporal nature of spoken speech, and show that it learns to extract both form and meaning-based linguistic knowledge from the input signal. We carry out an in-depth analysis of the representations used by different components of the trained model and show that encoding of semantic aspects tends to become richer as we go up the hierarchy of layers, whereas encoding of form-related aspects of the language input tends to initially increase and then plateau or decrease.

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Encoding of phonology in a recurrent neural model of grounded speech
Afra Alishahi | Marie Barking | Grzegorz Chrupała
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

We study the representation and encoding of phonemes in a recurrent neural network model of grounded speech. We use a model which processes images and their spoken descriptions, and projects the visual and auditory representations into the same semantic space. We perform a number of analyses on how information about individual phonemes is encoded in the MFCC features extracted from the speech signal, and the activations of the layers of the model. Via experiments with phoneme decoding and phoneme discrimination we show that phoneme representations are most salient in the lower layers of the model, where low-level signals are processed at a fine-grained level, although a large amount of phonological information is retain at the top recurrent layer. We further find out that the attention mechanism following the top recurrent layer significantly attenuates encoding of phonology and makes the utterance embeddings much more invariant to synonymy. Moreover, a hierarchical clustering of phoneme representations learned by the network shows an organizational structure of phonemes similar to those proposed in linguistics.

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Representation of Linguistic Form and Function in Recurrent Neural Networks
Ákos Kádár | Grzegorz Chrupała | Afra Alishahi
Computational Linguistics, Volume 43, Issue 4 - December 2017

We present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a standard standalone language model, and a multi-task gated recurrent network architecture consisting of two parallel pathways with shared word embeddings: The Visual pathway is trained on predicting the representations of the visual scene corresponding to an input sentence, and the Textual pathway is trained to predict the next word in the same sentence. We propose a method for estimating the amount of contribution of individual tokens in the input to the final prediction of the networks. Using this method, we show that the Visual pathway pays selective attention to lexical categories and grammatical functions that carry semantic information, and learns to treat word types differently depending on their grammatical function and their position in the sequential structure of the sentence. In contrast, the language models are comparatively more sensitive to words with a syntactic function. Further analysis of the most informative n-gram contexts for each model shows that in comparison with the Visual pathway, the language models react more strongly to abstract contexts that represent syntactic constructions.

2015

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Lingusitic Analysis of Multi-Modal Recurrent Neural Networks
Ákos Kádár | Grzegorz Chrupała | Afra Alishahi
Proceedings of the Fourth Workshop on Vision and Language

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Learning language through pictures
Grzegorz Chrupała | Ákos Kádár | Afra Alishahi
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2013

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Computational simulations of second language construction learning
Yevgen Matusevych | Afra Alishahi | Ad Backus
Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)

2012

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Concurrent Acquisition of Word Meaning and Lexical Categories
Afra Alishahi | Grzegorz Chrupala
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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Robust Semantic Analysis for Unseen Data in FrameNet
Alexis Palmer | Afra Alishahi | Caroline Sporleder
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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Online Entropy-Based Model of Lexical Category Acquisition
Grzegorz Chrupała | Afra Alishahi
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

2009

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Proceedings of the EACL 2009 Workshop on Cognitive Aspects of Computational Language Acquisition
Afra Alishahi | Thierry Poibeau | Aline Villavicencio
Proceedings of the EACL 2009 Workshop on Cognitive Aspects of Computational Language Acquisition

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Computational Modeling of Human Language Acquisition
Afra Alishahi
Tutorial Abstracts of ACL-IJCNLP 2009

2008

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Fast Mapping in Word Learning: What Probabilities Tell Us
Afra Alishahi | Afsaneh Fazly | Suzanne Stevenson
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning

2007

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A Cognitive Model for the Representation and Acquisition of Verb Selectional Preferences
Afra Alishahi | Suzanne Stevenson
Proceedings of the Workshop on Cognitive Aspects of Computational Language Acquisition

2005

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The Acquisition and Use of Argument Structure Constructions: A Bayesian Model
Afra Alishahi | Suzanne Stevenson
Proceedings of the Workshop on Psychocomputational Models of Human Language Acquisition