Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.
Abstractive summarization systems based on pretrained language models often generate coherent but factually inconsistent sentences.
This results in significant inference time speedups since the decoder-only architecture only needs to learn to interpret static encoder embeddings during inference.
The performance of state-of-the-art neural rankers can deteriorate substantially when exposed to noisy inputs or applied to a new domain.
In this work, we propose to leverage the prior information embedded in pretrained language models (LM) to improve generalization for intent classification and slot labeling tasks with limited training data.
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents.
Compositional reasoning tasks like multi-hop question answering, require making latent decisions to get the final answer, given a question.
A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document.
We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking.
Ranked #2 on Relation Classification on TACRED
Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM).
Ranked #3 on Semantic Parsing on spider
When multiple plausible answers are found, the system should rewrite the question for each answer to resolve the ambiguity.
Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable.
In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs).
Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate both answers and questions.
Generative models for Information Retrieval, where ranking of documents is viewed as the task of generating a query from a document's language model, were very successful in various IR tasks in the past.
In some cases, our model trained on synthetic data can even outperform the same model trained on real data
We set a new state-of-the-art for few-shot slot labeling, improving substantially upon the previous 5-shot ($75. 0\% \rightarrow 90. 9\%$) and 1-shot ($70. 4\% \rightarrow 81. 0\%$) state-of-the-art results.
Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks.
We introduce a multi-modal discriminative and generative frame-work capable of assisting humans in producing visual content re-lated to a given theme, starting from a collection of documents(textual, visual, or both).
Perceptual features (PFs) have been used with great success in tasks such as transfer learning, style transfer, and super-resolution.
We introduce a new approach to tackle the problem of offensive language in online social media.
Coreference resolution aims to identify in a text all mentions that refer to the same real-world entity.
Text attribute transfer using non-parallel data requires methods that can perform disentanglement of content and linguistic attributes.
We propose discriminative adversarial networks (DAN) for semi-supervised learning and loss function learning.
This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention.
In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora.
Ranked #10 on Text Summarization on DUC 2004 Task 1
Most state-of-the-art named entity recognition (NER) systems rely on handcrafted features and on the output of other NLP tasks such as part-of-speech (POS) tagging and text chunking.
Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features.
Ranked #16 on Relation Extraction on SemEval-2010 Task 8