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Found 23 papers, 5 papers with code
A Multi-Modal Multilingual Benchmark for Document Image Classification
Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents such as forms, emails, and other such documents.
Characterizing and Measuring Linguistic Dataset Drift
In this paper, we propose three dimensions of linguistic dataset drift: vocabulary, structural, and semantic drift.
Taxonomy Expansion for Named Entity Recognition
We experiment with 6 diverse datasets and show that PLM consistently performs better than most other approaches (0. 5 - 2. 5 F1), including in novel settings for taxonomy expansion not considered in prior work.
Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization
Opinion summarization provides an important solution for summarizing opinions expressed among a large number of reviews.
Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views
Temporal concept drift refers to the problem of data changing over time.
Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents
The standard classification setting where categories are fixed during both training and testing falls short in dynamic environments where new document categories could potentially emerge.
Efficient Classification of Long Documents Using Transformers
Several methods have been proposed for classifying long textual documents using Transformers.
Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics
Measuring bias is key for better understanding and addressing unfairness in NLP/ML models.
Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas
In entity linking, mentions of named entities in raw text are disambiguated against a knowledge base (KB).
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations.
Weakly Supervised Cross-lingual Semantic Relation Classification via Knowledge Distillation
Our classifier relies on a novel attention-based distillation approach to account for translation ambiguity when transferring knowledge from English to cross-lingual settings.
Robust Cross-lingual Hypernymy Detection using Dependency Context
We propose BISPARSE-DEP, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts.
Identifying Semantic Divergences in Parallel Text without Annotations
Recognizing that even correct translations are not always semantically equivalent, we automatically detect meaning divergences in parallel sentence pairs with a deep neural model of bilingual semantic similarity which can be trained for any parallel corpus without any manual annotation.
Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection
We introduce WHiC, a challenging testbed for detecting hypernymy, an asymmetric relation between words.
Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation
Parallel corpora are often not as parallel as one might assume: non-literal translations and noisy translations abound, even in curated corpora routinely used for training and evaluation.
The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives
While computers can now describe what is explicitly depicted in natural images, in this paper we examine whether they can understand the closure-driven narratives conveyed by stylized artwork and dialogue in comic book panels.
Parser for Abstract Meaning Representation using Learning to Search
We develop a novel technique to parse English sentences into Abstract Meaning Representation (AMR) using SEARN, a Learning to Search approach, by modeling the concept and the relation learning in a unified framework.
