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Found 15 papers, 9 papers with code
Contemporary NLP Modeling in Six Comprehensive Programming Assignments
We present a series of programming assignments, adaptable to a range of experience levels from advanced undergraduate to PhD, to teach students design and implementation of modern NLP systems.
Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models
Natural Language Inference (NLI) tasks involving temporal inference remain challenging for pre-trained language models (LMs).
Propagating Knowledge Updates to LMs Through Distillation
Then, we update the model parameters so that the distribution of the LM (the student) matches the distribution of the LM conditioned on the definition (the teacher) on the transfer set.
Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge
Pre-trained language models (LMs) are used for knowledge intensive tasks like question answering, but their knowledge gets continuously outdated as the world changes.
Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting
Through extensive human evaluation on EditBench, we find that object-masking during training leads to across-the-board improvements in text-image alignment -- such that Imagen Editor is preferred over DALL-E 2 and Stable Diffusion -- and, as a cohort, these models are better at object-rendering than text-rendering, and handle material/color/size attributes better than count/shape attributes.
Intermediate Entity-based Sparse Interpretable Representation Learning
However, while fine-tuning sparse, interpretable representations improves accuracy on downstream tasks, it destroys the semantics of the dimensions which were enforced in pre-training.
Entity Cloze By Date: What LMs Know About Unseen Entities
Given its wide coverage on entity knowledge and temporal indexing, our dataset can be used to evaluate LMs and techniques designed to modify or extend their knowledge.
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection
In this work, we empirically study how and whether such methods, applied in a bi-modal setting, can improve an existing VQA system's performance on the KBVQA task.
Cross-Lingual Fine-Grained Entity Typing
In this paper, we present a unified cross-lingual fine-grained entity typing model capable of handling over 100 languages and analyze this model's ability to generalize to languages and entities unseen during training.
CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge
We introduce CREAK, a testbed for commonsense reasoning about entity knowledge, bridging fact-checking about entities (Harry Potter is a wizard and is skilled at riding a broomstick) with commonsense inferences (if you're good at a skill you can teach others how to do it).
Biomedical Interpretable Entity Representations
Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable.
Modeling Fine-Grained Entity Types with Box Embeddings
Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling these types' complex interdependencies.
Ranked #9 on
Entity Typing
on Open Entity
Interpretable Entity Representations through Large-Scale Typing
On entity probing tasks involving recognizing entity identity, our embeddings used in parameter-free downstream models achieve competitive performance with ELMo- and BERT-based embeddings in trained models.
Fine-Grained Entity Typing for Domain Independent Entity Linking
For this problem, a domain is characterized not just by genre of text but even by factors as specific as the particular distribution of entities, as neural models tend to overfit by memorizing properties of frequent entities in a dataset.
Learning to Denoise Distantly-Labeled Data for Entity Typing
In this work, we propose a two-stage procedure for handling this type of data: denoise it with a learned model, then train our final model on clean and denoised distant data with standard supervised training.
Ranked #2 on
Entity Typing
on Ontonotes v5 (English)
