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Found 23 papers, 14 papers with code
DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models
We present DIALIGHT, a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems which facilitates systematic evaluations and comparisons between ToD systems using fine-tuning of Pretrained Language Models (PLMs) and those utilising the zero-shot and in-context learning capabilities of Large Language Models (LLMs).
EcomGPT-CT: Continual Pre-training of E-commerce Large Language Models with Semi-structured Data
Experimental results demonstrate the effectiveness of continual pre-training of E-commerce LLMs and the efficacy of our devised data mixing strategy.
Taxonomic analysis of asteroids with artificial neural networks
For the aim of analysis of the CSST spectroscopic data, we applied an algorithm using artificial neural networks (ANNs) to establish a preliminary classification model for asteroid taxonomy according to the design of the survey module of CSST.
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.
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
EcomInstruct scales up the data size and task diversity by constructing atomic tasks with E-commerce basic data types, such as product information, user reviews.
Exploring Lottery Prompts for Pre-trained Language Models
Consistently scaling pre-trained language models (PLMs) imposes substantial burdens on model adaptation, necessitating more efficient alternatives to conventional fine-tuning.
Zero-Shot Information Extraction via Chatting with ChatGPT
Zero-shot information extraction (IE) aims to build IE systems from the unannotated text.
Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing
We also found MCCE is very effective in fine-grained (130 types) and coarse-grained (9 types) entity typing.
Ranked #2 on
Entity Typing
on Open Entity
Few-shot Classification with Hypersphere Modeling of Prototypes
Moreover, it is more convenient to perform metric-based classification with hypersphere prototypes than statistical modeling, as we only need to calculate the distance from a data point to the surface of the hypersphere.
DAMO-NLP at NLPCC-2022 Task 2: Knowledge Enhanced Robust NER for Speech Entity Linking
Speech Entity Linking aims to recognize and disambiguate named entities in spoken languages.
Domain-Specific NER via Retrieving Correlated Samples
Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge.
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations
Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to satisfy.
Parallel Instance Query Network for Named Entity Recognition
Each instance query predicts one entity, and by feeding all instance queries simultaneously, we can query all entities in parallel.
Ranked #1 on
Nested Named Entity Recognition
on GENIA
Chinese Named Entity Recognition
named-entity-recognition
+5
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
Double Thompson Sampling in Finite stochastic Games
This algorithm achieves a total regret bound of $\tilde{\mathcal{O}}(D\sqrt{SAT})$in time horizon $T$ with $S$ states, $A$ actions and diameter $D$.
AISHELL-NER: Named Entity Recognition from Chinese Speech
Named Entity Recognition (NER) from speech is among Spoken Language Understanding (SLU) tasks, aiming to extract semantic information from the speech signal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Few-shot Learning with Big Prototypes
A big prototype could be effectively modeled by two sets of learnable parameters, one is the center of the hypersphere, which is an embedding with the same dimension of training examples.
Few-NERD: A Few-Shot Named Entity Recognition Dataset
In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types.
Ranked #5 on
Named Entity Recognition (NER)
on Few-NERD (SUP)
Prototypical Representation Learning for Relation Extraction
This approach allows us to learn meaningful, interpretable prototypes for the final classification.
Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation
In order to simultaneously alleviate these two issues, this paper proposes to couple distant annotation and adversarial training for cross-domain CWS.
DM\_NLP at SemEval-2018 Task 12: A Pipeline System for Toponym Resolution
This paper describes DM-NLP{'}s system for toponym resolution task at Semeval 2019.
Learning Models from Data with Measurement Error: Tackling Underreporting
In this paper we present a method for estimating the distribution of an outcome given a binary exposure that is subject to underreporting.
