데이터셋 탐색

WiC is a benchmark for the evaluation of context-sensitive word embeddings. WiC is framed as a binary classification task. Each instance in WiC has a target word w, either a verb or a noun, for which two contexts are provided. Each of these contexts triggers a specific meaning of w. The task is to identify if the occurrences of w in the two contexts correspond to the same meaning or not. In fact, the dataset can also be viewed as an application of Word Sense Disambiguation in practise. Source: WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations

A large multilingual benchmark, XL-WiC, featuring gold standards in 12 new languages from varied language families and with different degrees of resource availability, opening room for evaluation scenarios such as zero-shot cross-lingual transfer. Source: XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization

FoolMeTwice (FM2 for short) is a large dataset of challenging entailment pairs collected through a fun multi-player game. Gamification encourages adversarial examples, drastically lowering the number of examples that can be solved using "shortcuts" compared to other popular entailment datasets. Players are presented with two tasks. The first task asks the player to write a plausible claim based on the evidence from a Wikipedia page. The second one shows two plausible claims written by other players, one of which is false, and the goal is to identify it before the time runs out. Players "pay" to see clues retrieved from the evidence pool: the more evidence the player needs, the harder the claim. Game-play between motivated players leads to diverse strategies for crafting claims, such as temporal inference and diverting to unrelated evidence, and results in higher quality data for the entailment and evidence retrieval tasks.

WiC-TSV is a new multi-domain evaluation benchmark for Word Sense Disambiguation. More specifically, it is a framework for Target Sense Verification of Words in Context which grounds its uniqueness in the formulation as a binary classification task thus being independent of external sense inventories, and the coverage of various domains. This makes the dataset highly flexible for the evaluation of a diverse set of models and systems in and across domains. WiC-TSV provides three different evaluation settings, depending on the input signals provided to the model.

TorWIC is the dataset discussed in POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes. The purpose of this dataset is to evaluate the map mainteneance capabilities in a warehouse environment undergoing incremental changes. This dataset is collected in a Clearpath Robotics facility.