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20162026
most citedTweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder

134 citations · 452 across the 85 of their papers we have counts for

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Showing 2022Show all

12 papers · 1 filter

cs.CL2022★ 22 cited

Mind's Eye: Grounded Language Model Reasoning through Simulation

Ruibo Liu, Jason Wei, Shixiang Shane Gu +5

Successful and effective communication between humans and AI relies on a shared experience of the world. By training solely on written text, current language models (LMs) miss the…

cs.CL2022★ 54 cited

Language Models are Multilingual Chain-of-Thought Reasoners

Freda Shi, Mirac Suzgun, Markus Freitag +9

We evaluate the reasoning abilities of large language models in multilingual settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250…

cs.CL2022★ 6 cited

Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings

Yiren Jian, Chongyang Gao, Soroush Vosoughi

Semantic representation learning for sentences is an important and well-studied problem in NLP. The current trend for this task involves training a Transformer-based sentence encod…

cs.CL2022

Robin: A Novel Online Suicidal Text Corpus of Substantial Breadth and Scale

Daniel DiPietro, Vivek Hazari, Soroush Vosoughi

Suicide is a major public health crisis. With more than 20,000,000 suicide attempts each year, the early detection of suicidal intent has the potential to save hundreds of thousand…

cs.CL2022★ 1 cited

Interpretation Quality Score for Measuring the Quality of interpretability methods

Sean Xie, Soroush Vosoughi, Saeed Hassanpour

Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) tasks in recent years. In addition to making accurate decisions, the necessity o…

cs.CL2022

Contrastive Learning for Prompt-Based Few-Shot Language Learners

Yiren Jian, Chongyang Gao, Soroush Vosoughi

The impressive performance of GPT-3 using natural language prompts and in-context learning has inspired work on better fine-tuning of moderately-sized models under this paradigm. F…