Parinaz Sobhani


2021

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Shrinking Bigfoot: Reducing wav2vec 2.0 footprint
Zilun Peng | Akshay Budhkar | Ilana Tuil | Jason Levy | Parinaz Sobhani | Raphael Cohen | Jumana Nassour
Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing

Wav2vec 2.0 is a state-of-the-art speech recognition model which maps speech audio waveforms into latent representations. The largest version of wav2vec 2.0 contains 317 million parameters. Hence, the inference latency of wav2vec 2.0 will be a bottleneck in production, leading to high costs and a significant environmental footprint. To improve wav2vec’s applicability to a production setting, we explore multiple model compression methods borrowed from the domain of large language models. Using a teacher-student approach, we distilled the knowledge from the original wav2vec 2.0 model into a student model, which is 2 times faster, 4.8 times smaller than the original model. More importantly, the student model is 2 times more energy efficient than the original model in terms of CO2 emission. This increase in performance is accomplished with only a 7% degradation in word error rate (WER). Our quantized model is 3.6 times smaller than the original model, with only a 0.1% degradation in WER. To the best of our knowledge, this is the first work that compresses wav2vec 2.0.

2017

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A Dataset for Multi-Target Stance Detection
Parinaz Sobhani | Diana Inkpen | Xiaodan Zhu
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Current models for stance classification often treat each target independently, but in many applications, there exist natural dependencies among targets, e.g., stance towards two or more politicians in an election or towards several brands of the same product. In this paper, we focus on the problem of multi-target stance detection. We present a new dataset that we built for this task. Furthermore, We experiment with several neural models on the dataset and show that they are more effective in jointly modeling the overall position towards two related targets compared to independent predictions and other models of joint learning, such as cascading classification. We make the new dataset publicly available, in order to facilitate further research in multi-target stance classification.

2016

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DAG-Structured Long Short-Term Memory for Semantic Compositionality
Xiaodan Zhu | Parinaz Sobhani | Hongyu Guo
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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SemEval-2016 Task 6: Detecting Stance in Tweets
Saif Mohammad | Svetlana Kiritchenko | Parinaz Sobhani | Xiaodan Zhu | Colin Cherry
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Detecting Stance in Tweets And Analyzing its Interaction with Sentiment
Parinaz Sobhani | Saif Mohammad | Svetlana Kiritchenko
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

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A Dataset for Detecting Stance in Tweets
Saif Mohammad | Svetlana Kiritchenko | Parinaz Sobhani | Xiaodan Zhu | Colin Cherry
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We can often detect from a person’s utterances whether he/she is in favor of or against a given target entity (a product, topic, another person, etc.). Here for the first time we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets of interest―their stance. The targets of interest may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. The data pertains to six targets of interest commonly known and debated in the United States. Apart from stance, the tweets are also annotated for whether the target of interest is the target of opinion in the tweet. The annotations were performed by crowdsourcing. Several techniques were employed to encourage high-quality annotations (for example, providing clear and simple instructions) and to identify and discard poor annotations (for example, using a small set of check questions annotated by the authors). This Stance Dataset, which was subsequently also annotated for sentiment, can be used to better understand the relationship between stance, sentiment, entity relationships, and textual inference.

2015

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From Argumentation Mining to Stance Classification
Parinaz Sobhani | Diana Inkpen | Stan Matwin
Proceedings of the 2nd Workshop on Argumentation Mining

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Neural Networks for Integrating Compositional and Non-compositional Sentiment in Sentiment Composition
Xiaodan Zhu | Hongyu Guo | Parinaz Sobhani
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics