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PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification

, , , , and . (2015)

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Grounding Language to Landmarks in Arbitrary Outdoor Environments., , , , , and . ICRA, page 208-215. IEEE, (2020)Do Prompt-Based Models Really Understand the Meaning of their Prompts?, and . CoRR, (2021)Unit Testing for Concepts in Neural Networks., and . CoRR, (2022)Inducing Lexical Style Properties for Paraphrase and Genre Differentiation., and . HLT-NAACL, page 218-224. The Association for Computational Linguistics, (2015)Crowdsourcing for NLP., , and . HLT-NAACL, page 2-3. The Association for Computational Linguistics, (2015)Grounding Language to Non-Markovian Tasks with No Supervision of Task Specifications., , and . Robotics: Science and Systems, (2020)Planning with State Abstractions for Non-Markovian Task Specifications., , , , , and . Robotics: Science and Systems, (2019)What Happens To BERT Embeddings During Fine-tuning?, , , and . BlackboxNLP@EMNLP, page 33-44. Association for Computational Linguistics, (2020)PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification., , , , and . ACL (2), page 425-430. The Association for Computer Linguistics, (2015)Are Language Models Worse than Humans at Following Prompts? It's Complicated., , , and . EMNLP (Findings), page 7662-7686. Association for Computational Linguistics, (2023)