Month: September 2018

Clippers Tuesday 10/02 Adam Stiff on Explicitly Incorporating Tense/Aspect to Facilitate Creation of New Virtual Patients

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Explicitly Incorporating Tense/Aspect to Facilitate Creation of New Virtual Patients
The Virtual Patient project has collected a fair amount of data from student interactions with a patient presenting with back pain, but there is a desire to include a more diverse array of patients. With adequate training examples, treating the question identification task as a single label classification problem has been fairly successful. However, the current approach is not expected to work well to identify the novel questions that are important for patients with different circumstances, because these new questions have little training support. Exploring the label sets reveals some generalities across patients, including the importance of temporal properties of the symptoms. Including temporal information in the canonical question representations may allow us to leverage external data to mitigate the data sparsity issue for questions unique to new patients. I will solicit feedback on an approach to create a frame-like question representation that incorporates this temporal information, as revealed by the tense and linguistic aspect of clauses in the queries.

Clippers Tuesday 09/18: David King on paraphrase identification

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Alternate Uses for Domain Adaptation and Neural Machine Translation
Recent advances in Neural Machine Translation (NMT) have had ripple effects in other areas of NLP. The advances I am concerned with in this talk have to do with using NMT sentence encodings in downstream NLP tasks. After verifying an experiment where Wang et al. (2017) used this technique for sentence selection, I would now like to use this approach for paraphrase identification. In this talk, I will discuss Wang et al.’s experiment, my reimplementation, and my plans for integrating similar approaches for augmenting data used in the Virtual Patient project.

Clippers Tuesday: Alex Erdmann on processing Arabic dialects

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Tailoring “language agnostic” blackboxes to Arabic Dialects

Many state-of-the-art NLP technologies aspire to be language agnostic but perform disproportionately poorly on Arabic and its dialects. Identifying and understanding the linguistic phenomena which cause these performance drops and developing language specific solutions can shed light on how such technologies might be adapted to broaden their typological coverage. This talk will discuss several recent projects involving Arabic dialects which I worked on, including pan-dialectal dictionary induction, morphological modeling, and spelling normalization. For each of these projects, I will discuss the linguistic traits of Arabic that challenge language agnostic approaches, the language specific adaptations we employed to resolve such challenges, and finally, I will speculate on the generalizability of our solutions to other languages.

Clippers: Deblin Bagchi on Multilingual Speech Recognition

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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.