1 code implementation • EMNLP 2021 • Julian Martin Eisenschlos, Maharshi Gor, Thomas Müller, William W. Cohen
However, more than 20% of relational tables on the web have 20 or more rows (Cafarella et al., 2008), and these large tables present a challenge for current Transformer models, which are typically limited to 512 tokens.
Ranked #2 on
Question Answering
on HybridQA
no code implementations • 29 Jun 2021 • Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, William W. Cohen
We introduce a diagnostic dataset aimed at probing LMs for factual knowledge that changes over time and highlight problems with LMs at either end of the spectrum -- those trained on specific slices of temporal data, as well as those trained on a wide range of temporal data.
1 code implementation • Findings (ACL) 2021 • Syrine Krichene, Thomas Müller, Julian Martin Eisenschlos
To improve efficiency while maintaining a high accuracy, we propose a new architecture, DoT, a double transformer model, that decomposes the problem into two sub-tasks: A shallow pruning transformer that selects the top-K tokens, followed by a deep task-specific transformer that takes as input those K tokens.
1 code implementation • NAACL 2021 • Julian Martin Eisenschlos, Bhuwan Dhingra, Jannis Bulian, Benjamin Börschinger, Jordan Boyd-Graber
We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game.
no code implementations • SEMEVAL 2021 • Thomas Müller, Julian Martin Eisenschlos, Syrine Krichene
We adopt the binary TAPAS model of Eisenschlos et al. (2020) to this task.
1 code implementation • NAACL 2021 • Jonathan Herzig, Thomas Müller, Syrine Krichene, Julian Martin Eisenschlos
Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Julian Martin Eisenschlos, Syrine Krichene, Thomas Müller
To be able to use long examples as input of BERT models, we evaluate table pruning techniques as a pre-processing step to drastically improve the training and prediction efficiency at a moderate drop in accuracy.
Ranked #4 on
Table-based Fact Verification
on TabFact
1 code implementation • 29 Jun 2020 • Sebastian Prillo, Julian Martin Eisenschlos
While sorting is an important procedure in computer science, the argsort operator - which takes as input a vector and returns its sorting permutation - has a discrete image and thus zero gradients almost everywhere.
5 code implementations • ACL 2020 • Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno, Julian Martin Eisenschlos
In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms.
Ranked #1 on
Semantic Parsing
on SQA
4 code implementations • IJCNLP 2019 • Julian Martin Eisenschlos, Sebastian Ruder, Piotr Czapla, Marcin Kardas, Sylvain Gugger, Jeremy Howard
Pretrained language models are promising particularly for low-resource languages as they only require unlabelled data.
Ranked #1 on
Zero-shot Cross-Lingual Document Classification
on Cross-Lingual Sentiment (CLS)- English to German - DVD
Cross-Lingual Document Classification
Document Classification
+3