Search Results for author:
Found 14 papers, 7 papers with code
Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?
The practical success of much of NLP depends on the availability of training data.
Scene Graph Generation for Better Image Captioning?
We investigate the incorporation of visual relationships into the task of supervised image caption generation by proposing a model that leverages detected objects and auto-generated visual relationships to describe images in natural language.
Continuous Entailment Patterns for Lexical Inference in Context
If we allow for tokens outside the PLM's vocabulary, patterns can be adapted more flexibly to a PLM's idiosyncrasies.
Ranked #1 on
Few-Shot NLI
on SherLIiC
Position Information in Transformers: An Overview
Transformers are arguably the main workhorse in recent Natural Language Processing research.
Language Models for Lexical Inference in Context
Lexical inference in context (LIiC) is the task of recognizing textual entailment between two very similar sentences, i. e., sentences that only differ in one expression.
Ranked #2 on
Few-Shot NLI
on SherLIiC
Improving Scene Graph Classification by Exploiting Knowledge from Texts
We show that by fine-tuning the classification pipeline with the extracted knowledge from texts, we can achieve ~8x more accurate results in scene graph classification, ~3x in object classification, and ~1. 5x in predicate classification, compared to the supervised baselines with only 1% of the annotated images.
Ranking vs. Classifying: Measuring Knowledge Base Completion Quality
We randomly remove some of these correct answers from the data set, simulating the realistic scenario of real-world entities missing from a KB.
Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted Attention
Self-Attention Networks (SANs) are an integral part of successful neural architectures such as Transformer (Vaswani et al., 2017), and thus of pretrained language models such as BERT (Devlin et al., 2019) or GPT-3 (Brown et al., 2020).
Investigating Pretrained Language Models for Graph-to-Text Generation
We show that the PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further.
Ranked #1 on
KG-to-Text Generation
on WebNLG (All)
Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs
We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation.
Ranked #5 on
KG-to-Text Generation
on AGENDA
SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference
We present SherLIiC, a testbed for lexical inference in context (LIiC), consisting of 3985 manually annotated inference rule candidates (InfCands), accompanied by (i) ~960k unlabeled InfCands, and (ii) ~190k typed textual relations between Freebase entities extracted from the large entity-linked corpus ClueWeb09.
Ranked #1 on
Lexical Entailment
on SherLIiC
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing
To this end, we present the first approach to unsupervised text generation from KGs and show simultaneously how it can be used for unsupervised semantic parsing.
Ranked #1 on
Unsupervised KG-to-Text Generation
on WebNLG v2.1
Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks
In this work, we propose a new model for aspect-based sentiment analysis.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Embedding Learning Through Multilingual Concept Induction
We present a new method for estimating vector space representations of words: embedding learning by concept induction.
