Search Results
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
huggingface/transformers
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ICLR 2020
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities.
Attention Is All You Need
huggingface/transformers
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NeurIPS 2017
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
Ranked #2 on
Speech Emotion Recognition
on LSSED
Predicting Subjective Features of Questions of QA Websites using BERT
huggingface/transformers
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ICWR 2020
Community Question-Answering websites, such as StackOverflow and Quora, expect users to follow specific guidelines in order to maintain content quality.
Ranked #1 on
Question Quality Assessment
on CrowdSource QA
Efficient softmax approximation for GPUs
huggingface/transformers
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ICML 2017
We propose an approximate strategy to efficiently train neural network based language models over very large vocabularies.
DePlot: One-shot visual language reasoning by plot-to-table translation
Compared with a SOTA model finetuned on more than >28k data points, DePlot+LLM with just one-shot prompting achieves a 24. 0% improvement over finetuned SOTA on human-written queries from the task of chart QA.
Ranked #1 on
Chart Question Answering
on ChartQA
Transformers: State-of-the-Art Natural Language Processing
huggingface/transformers
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EMNLP 2020
Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks.
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
Our proposed framework provides access: (i) for verifying whether automatic metrics are faithful to human preference, regardless of their correlation level to human; and (ii) for inspecting the strengths and limitations of NLG systems via pairwise evaluation.
Cross-lingual Language Model Pretraining
huggingface/transformers
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NeurIPS 2019
On unsupervised machine translation, we obtain 34. 3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU.
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
huggingface/transformers
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TACL 2020
Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing.
Ranked #1 on
Split and Rephrase
on WikiSplit
Selecting Informative Contexts Improves Language Model Finetuning
Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final performance of language model fine-tuning.
