QA session1:
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• Large Product Key Memory for Pre-trained Language Models
• Exploring the Boundaries of Low-Resource BERT Distillation
• Keyphrase Generation with GANs in Low-Resources Scenarios
• Domain Adversarial Fine-Tuning as an Effective Regularizer

QA session2:
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• Don’t Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering
• A comparison between CNNs and WFAs for Sequence Classification
• Counterfactual Augmentation for Training Next Response Selection
• Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior

Invited Talk
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Mona Diab: Non-parameterized sentence encoders: an efficient alternative

QA session3-A:
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• Knowing Right from Wrong: Should We Use More Complex Models for Automatic Short-Answer Scoring in Bahasa Indonesia?
• Learning Informative Representations of Biomedical Relations with Latent Variable Models
• End to End Binarized Neural Networks for Text Classification
• Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks

QA session3-B:
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• PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding
• Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling
• Fixed Encoder Self-Attention Patterns in Transformer-Based Machine Translation

Invited speakers QA
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• Heng Ji: Efficient Language Acquisition through Multimedia Curriculum Learning
• Graham Neubig: More is Less? Non-parametric Language Models and Efficiency
• Alexander Rush: Advances in Sequence Knowledge Distillation
• Armand Joulin: Making Pre-trained Models More Sustainable

Panel Discussion
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Panelists: Kyunghyun Cho, Yejin Choi, Mona Diab, Yoav Goldberg, Iryna Gurevych, Heng Ji, Graham Neubig, Alexander Rush, Armand Joulin, Luke Zettlemoyer

QA session4:
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• Early Exiting BERT for Efficient Document Ranking
• Quasi-Multitask Learning: an Efficient Surrogate for Obtaining Model Ensembles
• Identifying Spurious Correlations for Robust Text Classification
• Improving QA Generalization by Concurrent Modeling of Multiple Biases

QA session5:
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• Load What You Need: Smaller Versions of Mutlilingual BERT
• Towards Accurate and Reliable Energy Measurement of NLP Models
• Do We Need to Create Big Datasets to Learn a Task?

QA Session6-A:
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• Rank and run-time aware compression of NLP Applications
• Incremental Neural Coreference Resolution in Constant Memory
• Efficient Estimation of Influence of a Training Instance
• Efficient Inference For Neural Machine Translation

QA Session6-B:
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• Guiding Attention for Self-Supervised Learning with Transformers
• Semi-supervised Formality Style Transfer using LanguageModel Discriminator and Mutual Information Maximization
• SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy
• Probabilstic Case-based Reasoning for Open-World Knowledge Graph Completion

QA Session7:
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• P-SIF: Document Embeddings using Partition Averaging
• Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm
• Analysis of Resource-efficient Predictive Models for Natural Language Processing
• A Little Bit Is Worse Than None: Ranking with Limited Training Data

QA Session8:
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• Doped Structured Matrices for Extreme Compression of LSTM Models
• A Two-stage Model for Slot Filling in Low-resource Settings: Domain-agnostic Non-slot Reduction and Pretrained Contextual Embeddings
• SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
• FastFormers: Highly Efficient Transformer Models for Natural Language Understanding
• OptSLA: an Optimization-Based Approach for Sequential Label Aggregation