Tailoring “language agnostic” blackboxes to Arabic Dialects
Learning from the best: A teacher-student framework multilingual models for low-resource languages.
Automatic Speech Recognition (ASR) in low resource languages is problematic because of the absence of transcripted speech. The amount of training data for any specific language in this category does not exceed 100 hours of speech. Recently, it has been found that knowledge obtained from a huge multilingual dataset (~ 1500 hours) is advantageous for ASR systems in low resource settings, i.e. the neural speech recognition models pre-trained on this dataset and then fine-tuned on language-specific data report a gain in performance as compared to training on language-specific data only. However, it goes without saying that a lot of time and resources are required to pre-train these models, specially the ones with recurrent connections. This work investigates the effectiveness of Teacher-Student (TS) learning to transfer knowledge from a recurrent speech recognition model (TDNN-LSTM) to a non-recurrent model (TDNN) in the context of multilingual speech recognition. Our results are interesting in more than one level. First, we find that student TDNN models trained using TS learning from a recurrent model (TDNN-LSTM) perform much better than their counterparts pre-trained using supervised learning. Second, these student models are trained only with language-specific data instead of the bulky multilingual dataset. Finally, the TS architecture allows us to leverage untranscribed data (previously untouched during supervised training) resulting in further improvement in the performance of the student TDNNs.
I’ll be presenting next Tuesday on incremental coreference as it relates to linguistic and psycholinguistic accuracy. Specifically, I’ll first discuss some human reading time results from coreference-based predictors, and reasons to think humans are processing coreference in an online way. The second part will cover ongoing work to add coreference prediction to an existing incremental left-corner parser, and give a sketch of linguistic and future psycholinguistic evaluation using such a parser.
Depth-bounding a grammar has been a popular technique for applying cognitively motivated restrictions to grammar induction algorithms to limit the search space of possible grammars. In this talk I will introduce two Bayesian depth-bounded grammar induction models for probabilistic context-free grammar from raw text. Both of them first depth-bound a normal PCFG and then sample trees using the depth-bounded PCFG but with different sampling algorithms. Several analyses are performed showing that depth-bounding is indeed effective in limiting the search space of the inducer. Results are also presented for successful unbounded PCFG induction with minimal constraints which has usually been thought to be very difficult. Parsing results on three different languages show that our models are able to produce parse trees better than or competitive with state-of-the-art constituency grammar induction models in terms of parsing accuracy.
This talk proposes deconvolutional time series regression (DTSR) — a general-purpose regression technique for modeling sequential data in which effects can reasonably be assumed to be temporally diffuse — and applies it to discover temporal structure in three existing psycholinguistic datasets. DTSR borrows from digital signal processing by recasting time series modeling as temporal deconvolution. It thus learns latent impulse response functions (IRF) that mediate the temporal relationship between two signals: the independent variable(s) on the one hand and the dependent variable on the other. Synthetic experiments show that DTSR successfully recovers true latent IRF, and psycholinguistic experiments demonstrate (1) important patterns of temporal diffusion that have not previously been quantified in psycholinguistic reading time experiments, (2) the ability to provide evidence for the absence of temporal diffusion, and (3) comparable (or in some cases substantially improved) prediction quality in comparison to more heavily parameterized statistical models. DTSR can thus be used to detect the existence of temporal diffusion and (when it exists) determine data driven impulse response functions to control for it. This suggests that DTSR can be an important component of any analysis pipeline for time series.
Evaluation Order Effects in Dynamic Continuized CCG:
From Negative Polarity Items to Balanced Punctuation
Combinatory Categorial Grammar’s (CCG; Steedman, 2000) flexible treatment of word order and constituency enable it to employ a compact lexicon, an important factor in its successful application to a range of NLP problems. However, its word order flexibility can be problematic for linguistic phenomena where linear order plays a key role. In this talk, I’ll show that the enhanced control over evaluation order afforded by Continuized CCG (Barker & Shan, 2014) makes it possible to formulate improved analyses of negative polarity items and balanced punctuation, and discuss their implementation as a refinement to a prototype parser for Dynamic Continuized CCG (White et al., 2017).
Hypertagging, or supertagging for realization, is the process of assigning CCG tags to predicates. Previous work has shown that it significantly increases realization speed and quality by reducing the search space of the realizer. This project seeks to improve on the current OpenCCG hypertagger, which uses a two-stage maximum entropy algorithm and reaches a dev accuracy of 95.1%. In this talk, I will present the results of various experiments using an LSTM hypertagger with different logical form linearization schemes. The performance with a pre-order linearization scheme is slightly under that of the current OpenCCG hypertagger, but the oracle linearization suggests that with a more English-like linearization, hypertagging with an LSTM is a promising way forward.
Word representations are a key technology in the NLP toolbox, but extending their success into representations of phrases and knowledge base entities has proven challenging. In this talk, I will present a method for jointly learning embeddings of words, phrases, and entities from uannotated text, using only a list of mappings between entities and surface forms. I compare these against prior methods that have relied on explicitly annotated text or the rich structure of knowledge graphs, and show that our learned embeddings better capture similarity and relatedness judgments and some relational domain knowledge.
I will also discuss experiments on augmenting the embedding model to learn soft entity disambiguation from contexts, and using member words to augment the learning of phrases. These additions harm model performance on some evaluations, and I will show some preliminary analysis of why the specific modeling approach for these ideas may not be the right one. I hope to brainstorm ideas on how to better model joint phrase-word learning and contextual disambiguation, as part of ongoing work.