@inproceedings{zhang-etal-2019-multi,
                  title = "Multi-Label Transfer Learning for Multi-Relational Semantic Similarity",
                  author = "Zhang, Li  and
                    Wilson, Steven  and
                    Mihalcea, Rada",
                  booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
                  month = jun,
                  year = "2019",
                  address = "Minneapolis, Minnesota",
                  publisher = "Association for Computational Linguistics",
                  url = "https://www.aclweb.org/anthology/S19-1005",
                  pages = "44--50",
                  abstract = "Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on. Yet, all the systems to date designed to capture such relations target one relation at a time. We propose a multi-label transfer learning approach based on LSTM to make predictions for several relations simultaneously and aggregate the losses to update the parameters. This multi-label regression approach jointly learns the information provided by the multiple relations, rather than treating them as separate tasks. Not only does this approach outperform the single-task approach and the traditional multi-task learning approach, but it also achieves state-of-the-art performance on all but one relation of the Human Activity Phrase dataset.",
              }
            

              @misc{zhang2018direct,
                  title={Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity},
                  author={Li Zhang and Steven R. Wilson and Rada Mihalcea},
                  year={2018},
                  eprint={1804.07835},
                  archivePrefix={arXiv},
                  primaryClass={cs.CL}
              }
            

              @InProceedings{acl18sql,
                  author    = {Catherine Finegan-Dollak\*  and  Jonathan K. Kummerfeld\*  and  Li Zhang  and  Karthik Ramanathan  and  Sesh Sadasivam  and  Rui Zhang  and  Dragomir Radev},
                  title     = {Improving Text-to-SQL Evaluation Methodology},
                  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
                  shortvenue = {ACL},
                  month     = {July},
                  year      = {2018},
                  address   = {Melbourne, Victoria, Australia},
                  pages     = {351--360},
                  abstract  = {To be informative, an evaluation must measure how well systems generalize to realistic unseen data. We identify limitations of and propose improvements to current evaluations of text-to-SQL systems. First, we compare human-generated and automatically generated questions, characterizing properties of queries necessary for real-world applications. To facilitate evaluation on multiple datasets, we release standardized and improved versions of seven existing datasets and one new text-to-SQL dataset. Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work. Finally, we demonstrate how the common practice of anonymizing variables during evaluation removes an important challenge of the task. Our observations highlight key difficulties, and our methodology enables effective measurement of future development.},
                  url       = {http://aclweb.org/anthology/P18-1033},
                  software  = {https://github.com/jkkummerfeld/text2sql-data},
                  data      = {https://github.com/jkkummerfeld/text2sql-data},
              }