output
20162026
most citedSwinVRNN: A Data-Driven Ensemble Forecasting Model via Learned Distribution Perturbation

65 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing 2021 · cs.CLShow all

6 papers · 2 filters

cs.CL2021★ 3 cited

Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

Lu Xu, Yew Ken Chia, Lidong Bing

Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion te…

cs.CL2021★ 2 cited

Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction

Jinpeng Zhang, Baijun Ji, Nini Xiao +4

Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another, and is typically through learning linear projections to align monolingual word…

cs.CL2021★ 14 cited

On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Ruidan He, Linlin Liu, Hai Ye +6

Adapter-based tuning has recently arisen as an alternative to fine-tuning. It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating t…

cs.CL2021★ 3 cited

G-Transformer for Document-level Machine Translation

Guangsheng Bao, Yue Zhang, Zhiyang Teng +2

Document-level MT models are still far from satisfactory. Existing work extend translation unit from single sentence to multiple sentences. However, study shows that when we furthe…

cs.CL2021★ 4 cited

Adaptive Nearest Neighbor Machine Translation

Xin Zheng, Zhirui Zhang, Junliang Guo +4

kNN-MT, recently proposed by Khandelwal et al. (2020a), successfully combines pre-trained neural machine translation (NMT) model with token-level k-nearest-neighbor (kNN) retrieval…

cs.CL2021★ 19 cited

Probing BERT in Hyperbolic Spaces

Boli Chen, Yao Fu, Guangwei Xu +4

Recently, a variety of probing tasks are proposed to discover linguistic properties learned in contextualized word embeddings. Many of these works implicitly assume these embedding…