161 research outputs found
Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models
Document-level Relation Extraction (DocRE), which aims to extract relations
from a long context, is a critical challenge in achieving fine-grained
structural comprehension and generating interpretable document representations.
Inspired by recent advances in in-context learning capabilities emergent from
large language models (LLMs), such as ChatGPT, we aim to design an automated
annotation method for DocRE with minimum human effort. Unfortunately, vanilla
in-context learning is infeasible for document-level relation extraction due to
the plenty of predefined fine-grained relation types and the uncontrolled
generations of LLMs. To tackle this issue, we propose a method integrating a
large language model (LLM) and a natural language inference (NLI) module to
generate relation triples, thereby augmenting document-level relation datasets.
We demonstrate the effectiveness of our approach by introducing an enhanced
dataset known as DocGNRE, which excels in re-annotating numerous long-tail
relation types. We are confident that our method holds the potential for
broader applications in domain-specific relation type definitions and offers
tangible benefits in advancing generalized language semantic comprehension
Adult Chinese Spanish L2ers’ acquisition of phi-agreement and temporal concord: The role of morphosyntactic features and adverb/subject-verb distance
PUBLISHED 22 December 2022While phi-agreement and concord are suggested to differ in nature during the first language (L1) acquisition, the acquisition of adverb-verb TC and SV person/number agreement by Chinese Spanish second language (L2) learners has only received limited attention. The current study examined morphosyntactic processing by advanced Chinese Spanish L2 learners (L2ers), whose L1 lacks the explicit morphological marking of tense and phi-agreement.This research was supported by National Social Science Fund of China (21FYYB011) and Young Scholar Incubation Plan of Xizang Minzu University (20MDX01)
DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning
Pragmatic reasoning plays a pivotal role in deciphering implicit meanings
that frequently arise in real-life conversations and is essential for the
development of communicative social agents. In this paper, we introduce a novel
challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic
reasoning and situated conversational understanding. Compared with previous
works that treat different figurative expressions (e.g. metaphor, sarcasm) as
individual tasks, DiPlomat provides a cohesive framework towards general
pragmatic understanding. Our dataset is created through the utilization of
Amazon Mechanical Turk ( AMT ), resulting in a total of 4, 177 multi-turn
dialogues. In conjunction with the dataset, we propose two tasks, Pragmatic
Identification and Reasoning (PIR) and Conversational Question Answering (CQA).
Experimental results with state-of-the-art (SOTA) neural architectures reveal
several significant findings: 1) large language models ( LLMs) exhibit poor
performance in tackling this subjective domain; 2) comprehensive comprehension
of context emerges as a critical factor for establishing benign human-machine
interactions; 3) current models defect in the application of pragmatic
reasoning. As a result, we call on more attention to improve the ability of
context understanding, reasoning, and implied meaning modeling
MoviePuzzle: Visual Narrative Reasoning through Multimodal Order Learning
We introduce MoviePuzzle, a novel challenge that targets visual narrative
reasoning and holistic movie understanding. Despite the notable progress that
has been witnessed in the realm of video understanding, most prior works fail
to present tasks and models to address holistic video understanding and the
innate visual narrative structures existing in long-form videos. To tackle this
quandary, we put forth MoviePuzzle task that amplifies the temporal feature
learning and structure learning of video models by reshuffling the shot, frame,
and clip layers of movie segments in the presence of video-dialogue
information. We start by establishing a carefully refined dataset based on
MovieNet by dissecting movies into hierarchical layers and randomly permuting
the orders. Besides benchmarking the MoviePuzzle with prior arts on movie
understanding, we devise a Hierarchical Contrastive Movie Clustering (HCMC)
model that considers the underlying structure and visual semantic orders for
movie reordering. Specifically, through a pairwise and contrastive learning
approach, we train models to predict the correct order of each layer. This
equips them with the knack for deciphering the visual narrative structure of
movies and handling the disorder lurking in video data. Experiments show that
our approach outperforms existing state-of-the-art methods on the \MoviePuzzle
benchmark, underscoring its efficacy
Energy-Based Generative Cooperative Saliency Prediction
Conventional saliency prediction models typically learn a deterministic
mapping from images to the corresponding ground truth saliency maps. In this
paper, we study the saliency prediction problem from the perspective of
generative models by learning a conditional probability distribution over
saliency maps given an image, and treating the prediction as a sampling
process. Specifically, we propose a generative cooperative saliency prediction
framework based on the generative cooperative networks, where a conditional
latent variable model and a conditional energy-based model are jointly trained
to predict saliency in a cooperative manner. We call our model the SalCoopNets.
The latent variable model serves as a fast but coarse predictor to efficiently
produce an initial prediction, which is then refined by the iterative Langevin
revision of the energy-based model that serves as a fine predictor. Such a
coarse-to-fine cooperative saliency prediction strategy offers the best of both
worlds. Moreover, we generalize our framework to the scenario of weakly
supervised saliency prediction, where saliency annotation of training images is
partially observed, by proposing a cooperative learning while recovering
strategy. Lastly, we show that the learned energy function can serve as a
refinement module that can refine the results of other pre-trained saliency
prediction models. Experimental results show that our generative model can
achieve state-of-the-art performance. Our code is publicly available at:
\url{https://github.com/JingZhang617/SalCoopNets}
Shuo Wen Jie Zi: Rethinking Dictionaries and Glyphs for Chinese Language Pre-training
We introduce CDBERT, a new learning paradigm that enhances the semantics
understanding ability of the Chinese PLMs with dictionary knowledge and
structure of Chinese characters. We name the two core modules of CDBERT as
Shuowen and Jiezi, where Shuowen refers to the process of retrieving the most
appropriate meaning from Chinese dictionaries and Jiezi refers to the process
of enhancing characters' glyph representations with structure understanding. To
facilitate dictionary understanding, we propose three pre-training tasks, i.e.,
Masked Entry Modeling, Contrastive Learning for Synonym and Antonym, and
Example Learning. We evaluate our method on both modern Chinese understanding
benchmark CLUE and ancient Chinese benchmark CCLUE. Moreover, we propose a new
polysemy discrimination task PolyMRC based on the collected dictionary of
ancient Chinese. Our paradigm demonstrates consistent improvements on previous
Chinese PLMs across all tasks. Moreover, our approach yields significant
boosting on few-shot setting of ancient Chinese understanding.Comment: To appear at ACL 2023 Finding
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