Search Results for author:
Found 10 papers, 8 papers with code
MATE: Multi-view Attention for Table Transformer Efficiency
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
Time-Aware Language Models as Temporal Knowledge Bases
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.
DoT: An efficient Double Transformer for NLP tasks with tables
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.
Fool Me Twice: Entailment from Wikipedia Gamification
We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game.
TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training
We adopt the binary TAPAS model of Eisenschlos et al. (2020) to this task.
Open Domain Question Answering over Tables via Dense Retrieval
Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages.
Understanding tables with intermediate pre-training
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
SoftSort: A Continuous Relaxation for the argsort Operator
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.
TAPAS: Weakly Supervised Table Parsing via Pre-training
In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms.
Ranked #1 on
Semantic Parsing
on SQA
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning
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
