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IBM Research at ACL 2020

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The 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), the premiere annual conference on AI and language, takes place July 5-10. As is the case with most events currently, ACL will be virtual this year due to COVID-19. At IBM Research AI, we’re excited to share with you — wherever you might be in the world — all the work we’ll have at ACL 2020 designed to advance AI for the enterprise.

The ability of AI to master language has been one of IBM Research AI’s key areas of focus for years. The field of Natural Language Processing (NLP) is constantly evolving in efforts to better outfit AI with the ability to communicate similarly to how us humans can. It’s an incredibly challenging area of research. An AI must identify, decipher, and navigate through natural language barriers — tasks like slang, idioms, acronyms, different languages and extracting meaning from multi-format documents, to name a few.

To tackle these challenges, IBM released earlier this year a new, four-part mastering language taxonomy that we believe will advance enterprise AI both through enhancing basic NLP features, as well as introducing more advanced tools and concepts. To learn more about this strategy and how we’re advancing it, click here.

Technical efforts at ACL 2020

We’re looking forward to once again having a robust presence at ACL that showcases a variety of IBM Research AI’s latest efforts in AI and language, including:

  • Automatic question generation (QG) for fast domain adaptation of enterprise QA systems
  • Automatic argument summarization leveraging technology from IBM Project Debater
  • Frequently Asked Questions (FAQ) retrieval
  • Automatic taxonomy construction
  • Insight analysis of the BERT (Bidirectional Encoder Representations from Transformers) NLP technique

Sponsorship and virtual booth

IBM Research AI is proudly sponsoring ACL 2020. We hope you will join us at our virtual booth to see and hear about our latest technology demos, publications and career opportunities including the AI Residency Program. 

For a full list of our papers and demos at the conference, see below.

Demos

  • GAhead Ask MAnything (GAMMA): GAAMA is a (multi-lingual) reading comprehension system for question-answering.
  • ExBERT: A Visual Tool to Explore BERTLearn how to uncover insights into what deep Transformer models understand about human language by interactively exploring their learned attentions and contextual embeddings.
  • NLQ over BAI: Natural Language Querying technology to dynamically generate insights from Business Process automation data.
  • Label Noise in Context (LNiC) : Uses training set context to increase precision and add explainability to a label noise detection system. Demo tool here and video here.
  • Vendor Master Scrubber: Identification and removal of duplicate vendors from vendor database using semantic clustering.
  • Cognitive Invoice Processing (ADAPT) : An automated tool consisting of semantic matching and semantic classifier to assist invoice processing in account payable process.

Accepted Papers at ACL 

On the Importance of Diversity in Question Generation for QA 

Arafat Sultan, Shubham Chandel, Ramon Astudillo, Vittorio Castelli

Out of the Echo Chamber: Detecting Countering Debate Speeches

Matan Orbach, Yonatan Bilu, Assaf Toledo, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim

Learning Implicit Text Generation via Feature Matching 

Inkit Padhi, Pierre Dognin, Ke Bai, Cicero Nogueira dos Santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das

A Multi-Perspective Architecture for Semantic Code Search

Rajarshi Haldar, Lingfei Wu, Jinjun Xiong, Julia Hockenmaier

From Arguments to Key Points: Towards Automatic Argument Summarization

Roy Bar-haim, Lilach Edelstein, Roni Friedman-melamed, Yoav Kantor, Dan Lahav, Noam Slonim

Improving Segmentation for Technical Support Problems

Abhirut Gupta, Kushal Chauhan

Interactive Construction of User-Centric Dictionary for Text Analytics

Ryosuke Kohita, Issei Yoshida, Hiroshi Kanayama, Tetsuya Nasukawa

Unsupervised FAQ Retrieval with Question Generation and BERT

Yosi Mass, Boaz Carmeli, Haggai Roitman, David Konopnicki

Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward

Luyang Huang, Lingfei Wu and Lu Wang

Crossing Variational Auto-encoders for Answer Retrieval

Wenhao Yu, Lingfei Wu, Qingkai Zeng, Yu Deng, Shu Tao, Meng Jiang

A Joint End-to-End Neural Model for Information Extraction with Global Features

Ying Lin, Heng Ji, Fei Huang, Lingfei Wu

The TechQA Dataset

Vittorio Castelli, Rishav Chakravarti, Saswati Dana, Anthony Ferritto, Hans Florian, Martin Franz, Dinesh Garg, Dinesh Khandelwal, Scott Mccarley, Mike Mccawley, Mohamed Nasr, Lin Pan, Cezar Pendus, John Pitrelli, Saurabh Pujar, Salim Roukos, Andy Sakrajda, Avi Sil, Rosario Uceda-sosa, Todd Ward, Rong Zhang

GPT-too: A language-model-first approach for AMR-to-text generation

Manuel Mager, Ramon Astudillo, Tahira Naseem, Arafat Sultan, Young-suk Lee, Hans Florian, Salim Roukos

Span Selection Pre-training for Question Answering

Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, G P Shrivatsa Bhargav, Dinesh Garg, Avi Sil

Taxonomy Construction via Graph-based Cross-Domain Knowledge Transfer

Chao Shang, Sarthak Dash, Md Faisal Mahbub Chowdhury, Nandana Mihindukulasooriya, Alfio Gliozzo,

ExBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer Models

Benjamin Hoover, Hendrik Strobelt, Sebastian Gehrmann

Implicit Discourse Relation Classification: We Need to Talk About Evaluation

Najoung Kim, Song Feng, Chulaka Gunasekara, Luis Lastras

HAT: Hardware-Aware Transformers for Efficient Neural Machine Translation

Hanrui Wang, Zhanghao Wu, Zhijian Liu, Han Cai, Ligeng Zhu, Chuang Gan, Song Han

Bridging Anaphora Resolution as Question Answering

Yufang Hou

Label Noise in Context
Michael Desmond, Catherine Finegan-Dollak, Jeff Boston, Matthew Arnold

 

 

 

 

Research Scientist and Team Lead, Question Answering, IBM Research AI

Karthik Sankaranarayanan

STSM, Senior Manager, IBM Research

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