This week, the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language (EMNLP-IJCNLP 2019) will take place in Hong Kong, China. EMNLP is one of the premiere conferences in Natural Language Processing (NLP) organized by the Association for Computational Linguistics special interest group on linguistic data (SIGDAT). The conference covers diverse research areas spanning computational approaches to natural language understanding, dialog, semantics, and discourse.
Adobe Research has been working in multimodal natural language processing and understanding and will be on hand to present recent research on dialog systems, semantics, question answering, summarization, and broadly applying NLP to build better intelligent systems.
If you’re attending EMNLP 2019, our researchers will be on hand to present their papers and workshop sessions. We also hope you can meet with us to discuss the unique opportunities at Adobe Research to explore and solve interesting natural language problems for the millions of people who use Adobe products. If you would like to schedule time at EMNLP to meet with our researchers, e-mail Walter Chang directly at firstname.lastname@example.org.
Accepted EMNLP publications:
- A Gated Self-attention Memory Network for Answer Selection (Tuan Lai, Quan Hung Tran, Trung Bui, and Daisuke Kihara)
- Let’s Ask Again: Refine Network for Automatic Question Generation (Preksha Nema, Akash Kumar Mohankumar, Mitesh M. Khapra, Balaji Vasan Srinivasan, and Balaraman Ravindran)
- DialogueGCN: Graph Convolutional Neural Network for Emotion Recognition in Conversation (Deepanway Ghosal, Navonil Majumder, Soujanya Poria , Niyati Chhaya, and Alexander Gelbukh)
- Multi-label Categorization of Accounts of Sexism using a Neural Framework (Pulkit Parikh, Harika Abburi, Pinkesh Badjatiya, Radhika Krishnan, Niyati Chhaya, Manish Gupta, and Vasudeva Varma)
- Generating Formality-tuned Summaries Using Input-dependent Rewards (Kushal Chawla, Balaji Vasan Srinivasan, and Niyati Chhaya) (paper for related conference, CoNLL: SIGNLL Conference on Computational Natural Language Learning)
- NewSum: Analyzing Sentence Fusion in Abstractive Summarization (Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, and Fei Liu)
- LOUHI: On the Effectiveness of the Pooling Methods for Biomedical Relation Extraction with Deep Learning (Tuan Ngo Nguyen, Franck Dernoncourt, and Thien Huu Nguyen)
A list of our NLP Adobe Research papers can be found on our website.
We’ll look forward to seeing you at EMNLP 2019!
Illustration by Claire (Qin) Li, Adobe Research