Papers by Aaron Jaech
Collecting Code-Switched Data from Social Media (L18-1)
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| Challenge: | a new method to identify code-switched data from the web is needed . code-witching is defined as the tendency of bilinguals to switch between languages . |
| Approach: | They propose a method that automatically collects code-switched tweets from the web . they use crowd-sourcing to obtain language identifiers for a subset of 8,000 tweets . |
| Outcome: | The proposed method identifies tweets as code-switched in languages L1 and L2 . it is compared to a Spanish-English corpus of code-witched tweets . |
Limitations of Autoregressive Models and Their Alternatives (2021.naacl-main)
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| Challenge: | Standard autoregressive language models only perform polynomial-time computation to compute probability of next symbol. |
| Approach: | authors propose alternative to standard autoregressive language models that use polynomial-time computation to compute probability of next symbol. |
| Outcome: | a large model size can grow superpolynomially in length, allowing it to store precomputed results and verify solutions. |
Personalized Language Model for Query Auto-Completion (P18-2)
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| Challenge: | Query auto-completion (QAC) is a search engine feature that suggests completed queries as the user types . recent work suggests personalization of the recurrent layer to generate personalized completions. |
| Approach: | They propose to use a recurrent neural network language model to generate personalized completions for search engines. |
| Outcome: | The proposed model can generate personalized completions for users not seen during training. |
Sparse Distillation: Speeding Up Text Classification by Using Bigger Student Models (2022.naacl-main)
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| Challenge: | Existing methods to reduce inference cost by distilling transformer models into lightweight student models are limited for high-volume use cases. |
| Approach: | They propose to distill state-of-the-art transformer models into lightweight student models to reduce computation cost at inference time. |
| Outcome: | The proposed pipeline achieves up to 600x speed-up on GPUs and CPUs on six single-sentence text classification tasks and in domain generalization settings. |
Community Member Retrieval on Social Media Using Textual Information (N18-2)
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| Challenge: | Existing methods for community membership detection using only text features are not effective. |
| Approach: | They propose an unsupervised task for learning user embeddings using text features . they propose a proxy task for embeddables that uses two embeddibles from the same account . |
| Outcome: | The proposed model is more effective than unsupervised representations of user embeddings with 16 different communities. |