Connectionist perspectives on language learning, representation and processing.

The field of formal linguistics was founded on the premise that language is mentally represented as a deterministic symbolic grammar. While this approach has captured many important characteristics of the world\u27s languages, it has also led to a tendency to focus theoretical questions on the correct formalization of grammatical rules while also de-emphasizing the role of learning and statistics in language development and processing. In this review we present a different approach to language research that has emerged from the parallel distributed processing or \u27connectionist\u27 enterprise. In the connectionist framework, mental operations are studied by simulating learning and processing within networks of artificial neurons. With that in mind, we discuss recent progress in connectionist models of auditory word recognition, reading, morphology, and syntactic processing. We argue that connectionist models can capture many important characteristics of how language is learned, represented, and processed, as well as providing new insights about the source of these behavioral patterns. Just as importantly, the networks naturally capture irregular (non-rule-like) patterns that are common within languages, something that has been difficult to reconcile with rule-based accounts of language without positing separate mechanisms for rules and exceptions

Sensitivity of human auditory cortex to rapid frequency modulation revealed by multivariate representational similarity analysis.

Functional Magnetic Resonance Imaging (fMRI) was used to investigate the extent, magnitude, and pattern of brain activity in response to rapid frequency-modulated sounds. We examined this by manipulating the direction (rise vs. fall) and the rate (fast vs. slow) of the apparent pitch of iterated rippled noise (IRN) bursts. Acoustic parameters were selected to capture features used in phoneme contrasts, however the stimuli themselves were not perceived as speech per se. Participants were scanned as they passively listened to sounds in an event-related paradigm. Univariate analyses revealed a greater level and extent of activation in bilateral auditory cortex in response to frequency-modulated sweeps compared to steady-state sounds. This effect was stronger in the left hemisphere. However, no regions showed selectivity for either rate or direction of frequency modulation. In contrast, multivoxel pattern analysis (MVPA) revealed feature-specific encoding for direction of modulation in auditory cortex bilaterally. Moreover, this effect was strongest when analyses were restricted to anatomical regions lying outside Heschl\u27s gyrus. We found no support for feature-specific encoding of frequency modulation rate. Differential findings of modulation rate and direction of modulation are discussed with respect to their relevance to phonetic discrimination

Cross-lingual Distillation for Text Classification

Cross-lingual text classification(CLTC) is the task of classifying documents written in different languages into the same taxonomy of categories. This paper presents a novel approach to CLTC that builds on model distillation, which adapts and extends a framework originally proposed for model compression. Using soft probabilistic predictions for the documents in a label-rich language as the (induced) supervisory labels in a parallel corpus of documents, we train classifiers successfully for new languages in which labeled training data are not available. An adversarial feature adaptation technique is also applied during the model training to reduce distribution mismatch. We conducted experiments on two benchmark CLTC datasets, treating English as the source language and German, French, Japan and Chinese as the unlabeled target languages. The proposed approach had the advantageous or comparable performance of the other state-of-art methods.Comment: Accepted at ACL 2017; Code available at https://github.com/xrc10/cross-distil

Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201