3 research outputs found
JCoLA: Japanese Corpus of Linguistic Acceptability
Neural language models have exhibited outstanding performance in a range of
downstream tasks. However, there is limited understanding regarding the extent
to which these models internalize syntactic knowledge, so that various datasets
have recently been constructed to facilitate syntactic evaluation of language
models across languages. In this paper, we introduce JCoLA (Japanese Corpus of
Linguistic Acceptability), which consists of 10,020 sentences annotated with
binary acceptability judgments. Specifically, those sentences are manually
extracted from linguistics textbooks, handbooks and journal articles, and split
into in-domain data (86 %; relatively simple acceptability judgments extracted
from textbooks and handbooks) and out-of-domain data (14 %; theoretically
significant acceptability judgments extracted from journal articles), the
latter of which is categorized by 12 linguistic phenomena. We then evaluate the
syntactic knowledge of 9 different types of Japanese language models on JCoLA.
The results demonstrated that several models could surpass human performance
for the in-domain data, while no models were able to exceed human performance
for the out-of-domain data. Error analyses by linguistic phenomena further
revealed that although neural language models are adept at handling local
syntactic dependencies like argument structure, their performance wanes when
confronted with long-distance syntactic dependencies like verbal agreement and
NPI licensing
SoccerNet 2023 Challenges Results
peer reviewedThe SoccerNet 2023 challenges were the third annual video understanding
challenges organized by the SoccerNet team. For this third edition, the
challenges were composed of seven vision-based tasks split into three main
themes. The first theme, broadcast video understanding, is composed of three
high-level tasks related to describing events occurring in the video
broadcasts: (1) action spotting, focusing on retrieving all timestamps related
to global actions in soccer, (2) ball action spotting, focusing on retrieving
all timestamps related to the soccer ball change of state, and (3) dense video
captioning, focusing on describing the broadcast with natural language and
anchored timestamps. The second theme, field understanding, relates to the
single task of (4) camera calibration, focusing on retrieving the intrinsic and
extrinsic camera parameters from images. The third and last theme, player
understanding, is composed of three low-level tasks related to extracting
information about the players: (5) re-identification, focusing on retrieving
the same players across multiple views, (6) multiple object tracking, focusing
on tracking players and the ball through unedited video streams, and (7) jersey
number recognition, focusing on recognizing the jersey number of players from
tracklets. Compared to the previous editions of the SoccerNet challenges, tasks
(2-3-7) are novel, including new annotations and data, task (4) was enhanced
with more data and annotations, and task (6) now focuses on end-to-end
approaches. More information on the tasks, challenges, and leaderboards are
available on https://www.soccer-net.org. Baselines and development kits can be
found on https://github.com/SoccerNet
SoccerNet 2023 challenges results
SoccerNet 2023 Challenges ResultsThe SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet