Search Results for author:
Found 9 papers, 6 papers with code
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which always have restricted output and input format.
Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model
We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance.
DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition
Also, we discover that the limited context length causes the retrieval knowledge to be invisible to the model.
Multilingual Named Entity Recognition
named-entity-recognition
+4
Named Entity and Relation Extraction with Multi-Modal Retrieval
MoRe contains a text retrieval module and an image-based retrieval module, which retrieve related knowledge of the input text and image in the knowledge corpus respectively.
Ranked #1 on
Multi-modal Named Entity Recognition
on SNAP (MNER)
Multi-modal Named Entity Recognition
Named Entity Recognition
+4
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition
Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
Multilingual Named Entity Recognition
Named Entity Recognition
+1
Deep Inside-outside Recursive Autoencoder with All-span Objective
Deep inside-outside recursive autoencoder (DIORA) is a neural-based model designed for unsupervised constituency parsing.
An Empirical Comparison of Unsupervised Constituency Parsing Methods
Unsupervised constituency parsing aims to learn a constituency parser from a training corpus without parse tree annotations.
Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders
Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations.
CRF Autoencoder for Unsupervised Dependency Parsing
The encoder part of our model is discriminative and globally normalized which allows us to use rich features as well as universal linguistic priors.
Dependency Grammar Induction
Unsupervised Dependency Parsing
