Deep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, dataset, and task. We introduce the Natural Language Decathlon (decaNLP), a challenge that spans ten tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, zero-shot relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution. We cast all tasks as question answering over a context. Furthermore, we present a new Multitask Question Answering Network (MQAN) jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. MQAN shows improvements in transfer learning for machine translation and named entity recognition, domain adaptation for sentiment analysis and natural language inference, and zero-shot capabilities for text classification. We demonstrate that the MQAN's multi-pointer-generator decoder is key to this success and performance further improves with an anti-curriculum training strategy. Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting. We also release code for procuring and processing data, training and evaluating models, and reproducing all experiments for decaNLP.
The Natural Language Decathlon is a multitask challenge that spans ten tasks: question answering (SQuAD), machine translation (IWSLT), summarization (CNN/DM), natural language inference (MNLI), sentiment analysis (SST), semantic role labeling(QA‑SRL), zero-shot relation extraction (QA‑ZRE), goal-oriented dialogue (WOZ, semantic parsing (WikiSQL), and commonsense reasoning (MWSC). Each task is cast as question answering, which makes it possible to use our new Multitask Question Answering Network (MQAN). This model jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. For a more thorough introduction to decaNLP and the tasks, see the main website, our blog post, or the paper. While the research direction associated with this repository focused on multitask learning, the framework itself is designed in a way that should make single-task training, transfer learning, and zero-shot evaluation simple.
Salesforce Research has created a natural language processing architecture that can handle multiple models and tasks. Typically, natural language processing (NLP) has a model for each function such as translation, sentiment analysis and question and answer. The research, led by Salesforce Chief Scientist Richard Socher, revolves around a challenge dubbed Natural Language Decathalon (decaNLP). The challenge spans 10 tasks--question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, relation extraction, goal-oriented dialogue, database query generation, and pronoun resolution--and feeds into a system that jointly learns. Think of decaNLP as a Swiss Army Knife for natural language processing.
Most research on lifelong learning (LLL) applies to images or games, but not language. Here, we introduce LAMAL, a simple yet effective method for LLL based on language modeling. LAMAL replays pseudo samples of previous tasks while requiring no extra memory or model capacity. To be specific, LAMAL is a language model learning to solve the task and generate training samples at the same time. At the beginning of training a new task, the model generates some pseudo samples of previous tasks to train alongside the data of the new task. The results show that LAMAL prevents catastrophic forgetting without any sign of intransigence and can solve up to five very different language tasks sequentially with only one model. Overall, LAMAL outperforms previous methods by a considerable margin and is only 2-3\% worse than multitasking which is usually considered as the upper bound of LLL. Our source code is available at https://github.com/xxx.
Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.