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Found 33 papers, 11 papers with code
H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network
Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
In this report, we introduce the Gemini 1. 5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio.
Memory Augmented Language Models through Mixture of Word Experts
We demonstrate that MoWE performs significantly better than the T5 family of models with similar number of FLOPs in a variety of NLP tasks.
Triggering Multi-Hop Reasoning for Question Answering in Language Models using Soft Prompts and Random Walks
Despite readily memorizing world knowledge about entities, pre-trained language models (LMs) struggle to compose together two or more facts to perform multi-hop reasoning in question-answering tasks.
Knowledge Prompts: Injecting World Knowledge into Language Models through Soft Prompts
The resulting soft knowledge prompts (KPs) are task independent and work as an external memory of the LMs.
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization
Abstractive summarization systems based on pretrained language models often generate coherent but factually inconsistent sentences.
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference
This results in significant inference time speedups since the decoder-only architecture only needs to learn to interpret static encoder embeddings during inference.
Contrastive Fine-tuning Improves Robustness for Neural Rankers
The performance of state-of-the-art neural rankers can deteriorate substantially when exposed to noisy inputs or applied to a new domain.
Joint Text and Label Generation for Spoken Language Understanding
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.
Improving Factual Consistency of Abstractive Summarization via Question Answering
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.
Generative Context Pair Selection for Multi-hop Question Answering
Compositional reasoning tasks like multi-hop question answering, require making latent decisions to get the final answer, given a question.
Entity-level Factual Consistency of Abstractive Text Summarization
A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document.
Structured Prediction as Translation between Augmented Natural Languages
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 #3 on
Relation Classification
on TACRED
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
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 #7 on
Semantic Parsing
on spider
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction
When multiple plausible answers are found, the system should rewrite the question for each answer to resolve the ambiguity.
Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling
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.
Cross-Lingual Document Classification
Document Classification
+2
DualTKB: A Dual Learning Bridge between Text and Knowledge Base
In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs).
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate both answers and questions.
Beyond [CLS] through Ranking by Generation
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.
Embedding-based Zero-shot Retrieval through Query Generation
In some cases, our model trained on synthetic data can even outperform the same model trained on real data
Augmented Natural Language for Generative Sequence Labeling
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.
Learning Implicit Text Generation via Feature Matching
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.
Covering the News with (AI) Style
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).
Generative Feature Matching Networks
We propose a non-adversarial feature matching-based approach to train generative models.
Learning Implicit Generative Models by Matching Perceptual Features
Perceptual features (PFs) have been used with great success in tasks such as transfer learning, style transfer, and super-resolution.
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer
We introduce a new approach to tackle the problem of offensive language in online social media.
Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering
Coreference resolution aims to identify in a text all mentions that refer to the same real-world entity.
Learning Loss Functions for Semi-supervised Learning via Discriminative Adversarial Networks
We propose discriminative adversarial networks (DAN) for semi-supervised learning and loss function learning.
A Structured Self-attentive Sentence Embedding
This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention.
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
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
Boosting Named Entity Recognition with Neural Character Embeddings
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.
Classifying Relations by Ranking with Convolutional Neural Networks
Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features.
Ranked #27 on
Relation Extraction
on SemEval-2010 Task-8
