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Found 11 papers, 5 papers with code
MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision
In this work, we explore multilingual sequence labeling with minimal supervision using a single unified model for multiple languages.
Survey for Landing Generative AI in Social and E-commerce Recsys -- the Industry Perspectives
Recently, generative AI (GAI), with their emerging capabilities, have presented unique opportunities for augmenting and revolutionizing industrial recommender systems (Recsys).
Short Text Pre-training with Extended Token Classification for E-commerce Query Understanding
When applying masking to short search queries, most contextual information is lost and the intent of the search queries may be changed.
Condensing Graphs via One-Step Gradient Matching
However, existing approaches have their inherent limitations: (1) they are not directly applicable to graphs where the data is discrete; and (2) the condensation process is computationally expensive due to the involved nested optimization.
SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models
SeqZero achieves SOTA performance of BART-based models on GeoQuery and EcommerceQuery, which are two few-shot datasets with compositional data split.
RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph
And meanwhile, RETE autoregressively accumulates retrieval-enhanced user representations from each time step, to capture evolutionary patterns for joint query and product prediction.
QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction
We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms.
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data
Unfortunately, we observe that weakly labeled data does not necessarily improve, or even deteriorate the model performance (due to the extensive noise in the weak labels) when we train deep NER models over a simple or weighted combination of the strongly labeled and weakly labeled data.
Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning
In this paper, we explore to improve pretrained models with label hierarchies on the ZS-MTC task.
On Data Augmentation for Extreme Multi-label Classification
Our contributions are two-factored: (1) we introduce a new state-of-the-art classifier that uses label attention with RoBERTa and combine it with our augmentation framework for further improvement; (2) we present a broad study on how effective are different augmentation methods in the XMC task.
Predicting Driver Attention in Critical Situations
Because critical driving moments are so rare, collecting enough data for these situations is difficult with the conventional in-car data collection protocol---tracking eye movements during driving.
