The availability of corpora has led to significant advances in training semantic parsers in English.
Biases continue to be prevalent in modern text and media, especially subjective bias – a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth.
Our method progressively increases the training length throughout the pretraining phase, thereby mitigating computational costs and enhancing efficiency.
To address this limitation, we propose an alternative perspective, situated NLE, including a situated generation framework and a situated evaluation framework.
We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e. g., "Digital Cameras", generating a list of complementary concepts, e. g., 1) Camera Lenses 2) Batteries 3) Camera Cases 4) Memory Cards 5) Battery Chargers.
This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks.
This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones.
With the demanding need for deploying dialogue systems in new domains with less cost, zero-shot dialogue state tracking (DST), which tracks user's requirements in task-oriented dialogues without training on desired domains, draws attention increasingly.
First, we use this system to stress tests question answering, machine translation, and semantic parsing.
Although it has been demonstrated that Natural Language Processing (NLP) algorithms are vulnerable to deliberate attacks, the question of whether such weaknesses can lead to software security threats is under-explored.
SeqZero achieves SOTA performance of BART-based models on GeoQuery and EcommerceQuery, which are two few-shot datasets with compositional data split.
Existing models for table understanding require linearization of the table structure, where row or column order is encoded as an unwanted bias.
This strengthens the local feature invariance for the resampled features and enables detecting vehicles in an arbitrary orientation.
The availability of corpora to train semantic parsers in English has lead to significant advances in the field.
Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text.