Gradient-based Analysis of NLP Models is Manipulable. The Google research team suggests a unified approach to transfer learning in NLP with the goal to set a new state of the art in the field. Reinforcement learning. ELECTRA: Pre-training Text […] The post The Best NLP Papers From ICLR 2020 appeared first on TOPBOTS. I welcome any feedback on this list. The Longformer model achieves a new state of the art on character-level language modeling tasks: After pre-training and fine-tuning for six tasks, including classification, question answering, and coreference resolution, the Longformer-base consistently outperformers the RoBERTa-base with: The performance gains are especially remarkable for the tasks that require a long context (i.e., WikiHop and Hyperpartisan). Automatic metrics are used as a proxy for human translation evaluation, which is considerably more expensive and time-consuming. As a result, the model learns from all input tokens instead of the small masked fraction, making it much more computationally efficient. In both cases, work from industry and … We then discuss the limitations of this work by analyzing failure cases of our models. Even though it works quite well, this approach is not particularly data-efficient as it learns from only a small fraction of tokens (typically ~15%). In contrast to most modern conversational agents, which are highly specialized, the Google research team introduces a chatbot Meena that can chat about virtually anything. Out of 77K collected questions, 53K were deemed valid. Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer. Such a framework allows using the same model, objective, training procedure, and decoding process for different tasks, including summarization, sentiment analysis, question answering, and machine translation. This approach works by using non-parallel data from two domains as a partially observed parallel corpus. Honorable Mention Papers – Main Conference. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. The mode understands which tasks should be performed thanks to the task-specific prefix added to the original input sentence (e.g., “translate English to German:”, “summarize:”). Accepted Papers Learning for E-Learning. Despite recent progress, open-domain chatbots still have significant weaknesses: their responses often do not make sense or are too vague or generic. At the same time, we also identify some datasets where GPT-3’s few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. To address this problem, the Google Research team introduces several techniques that improve the efficiency of Transformers. Conducting a thorough analysis of automatic metrics performance metrics vs. human judgments in machine translation, and providing key recommendations on evaluating MT systems: Giving preference to such evaluation metrics as chrF, YiSi-1, and ESIM over BLEU and TER. Together, these findings suggest improvements to the protocols for metric evaluation and system performance evaluation in machine translation. but it still has serious weaknesses and sometimes makes very silly mistakes. I have received a version of this myself at ACL 2020: the new corpus presented in this work is not larger than the existing corpora. This list is compiled by Masato Hagiwara. The code itself is not available, but some dataset statistics together with unconditional, unfiltered 2048-token samples from GPT-3 are released on. GOOD WRITE-UP. This attention mechanism scales linearly with the sequence length and enables processing of documents with thousands of tokens. The Winograd Schema Challenge (WSC) (Levesque, Davis, and Morgenstern 2011), a benchmark for commonsense reasoning, is a set of 273 expert-crafted pronoun resolution problems originally designed to be unsolvable for statistical models that rely on selectional preferences or word associations. On the other hand, they raise a concern that we are likely to be overestimating the true capabilities of machine commonsense across all these benchmarks. The RoBERTa-based model fine-tuned on WinoGrande achieved a new state of the art on WSC and four other related datasets: The paper received the Outstanding Paper Award at AAAI 2020, one of the key conferences in artificial intelligence. Posted on May 7, 2020 ai news. The authors claim that existing benchmarks for commonsense reasoning suffer from systematic bias and annotation artifacts, leading to overestimation of the true capabilities of machine intelligence on commonsense reasoning. Moving away from greedy algorithms like beam search will help performance on downstream tasks. This has implications for building more robust dialogue applications. Results on the sentiment transfer, author imitation, and formality transfer, Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Main Contribution: This paper describes a new language model that captures both the position of words and their order relationships. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Furthermore, the researchers show that WinoGrande is an effective resource for transfer learning, by using a RoBERTa model fine-tuned with WinoGrande to achieve new state-of-the-art results on WSC and four other related benchmarks. Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Thorough experiments demonstrate this new pre-training task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out. Crowdworkers were asked to write twin sentences that meet the WSC requirements and contain certain anchor words. I welcome any feedback on this list. By analyzing the introduced techniques one by one, the authors show that model accuracy is not sacrificed by: switching to locality-sensitive hashing attention; Reformer performs on par with the full Transformer model while demonstrating much higher speed and memory efficiency: The suggested efficiency improvements enable more widespread Transformer application, especially for the tasks that depend on large-context data, such as: The official code implementation from Google is publicly available on, The PyTorch implementation of Reformer is also available on. To address this problem, the research team introduces, CheckList provides users with a list of linguistic, Then, to break down potential capability failures into specific behaviors, CheckList suggests different. For the first time, a single deep RL agent—Agent57 (Badia et al., 2020)—has … Technology trend. The suggested Longformer model employs an attention pattern that combines local windowed attention with task-motivated global attention. Conclusion. The leading Transformer models have become so big that they can be realistically trained only in large research laboratories. Main Contribution: A commonly used task for pre-training language models is to mask the input and have the model predict what is masked. Top NLP Libraries to Use 2020 AllenNLP, Fast.ai, Spacy, NLTK, TorchText, Huggingface, Gensim, OpenNMT, ParlAI, DeepPavlov The alternative approaches are usually designed for evaluation of specific behaviors on individual tasks and thus, lack comprehensiveness. The original TensorFlow implementation and pre-trained weights are released on, However, in contrast to GPT-2, it uses alternating dense and locally banded sparse attention patterns in the layers of the transformer, as in the. Browse 341 tasks • 919 datasets • 872 . The computational requirements of self-attention grow quadratically with sequence length, making it hard to process on current hardware. They show that an adversary does not need any real training data to mount the attack successfully. Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya, Main Contribution: The authors propose a new transformer model with two major improvements to the architecture: a) using reversible layers to prevent the need of storing the activations of all layers for backpropagation, and b) using locality sensitive hashing to approximate the costly softmax(QK^T) computation in the full dot-product attention. In another user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it. Here are the papers found and why they matter. However. The author’s Transformer Complex-Order model outperforms the Vanilla Transformer and complex-vanilla Transformer by 1.3 and 1.1 in absolute BLEU score respectively. Demonstrating that a large-scale low-perplexity model can be a good conversationalist: The best end-to-end trained Meena model outperforms existing state-of-the-art open-domain chatbots by a large margin, achieving an SSA score of 72% (vs. 56%). The experiments confirm that the introduced approach leads to significantly faster training and higher accuracy on downstream NLP tasks. Self-attention is one of the key factors behind the success of Transformer architecture. 100 Must-Read NLP Papers. A research work, Semantic Meaning Based Bengali Web Text Categorization Using Deep Convolutional … The Best of Applied Artificial Intelligence, Machine Learning, Automation, Bots, Chatbots.
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