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Found 57 papers, 15 papers with code
On the Effectiveness of Sentence Encoding for Intent Detection Meta-Learning
Recent studies on few-shot intent detection have attempted to formulate the task as a meta-learning problem, where a meta-learning model is trained with a certain capability to quickly adapt to newly specified few-shot tasks with potentially unseen intent categories.
DesignProbe: A Graphic Design Benchmark for Multimodal Large Language Models
This complexity makes the comprehension of graphic design challenging, for it needs the capability to both recognize the design elements and understand the design.
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
The challenge is that information entropy may be a suboptimal compression metric: (i) it only leverages unidirectional context and may fail to capture all essential information needed for prompt compression; (ii) it is not aligned with the prompt compression objective.
Desigen: A Pipeline for Controllable Design Template Generation
In this paper, we present Desigen, an automatic template creation pipeline which generates background images as well as harmonious layout elements over the background.
Spot the Error: Non-autoregressive Graphic Layout Generation with Wireframe Locator
Furthermore, based on our observation that pixel space is more sensitive in capturing spatial patterns of graphic layouts (e. g., overlap, alignment), we propose a learning-based locator to detect erroneous tokens which takes the wireframe image rendered from the generated layout sequence as input.
All Data on the Table: Novel Dataset and Benchmark for Cross-Modality Scientific Information Extraction
To close this gap in data availability and enable cross-modality IE, while alleviating labeling costs, we propose a semi-supervised pipeline for annotating entities in text, as well as entities and relations in tables, in an iterative procedure.
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs' perception of the key information to simultaneously address the three challenges.
LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models
Large language models (LLMs) have been applied in various applications due to their astonishing capabilities.
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition
Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language.
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text
Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-distribution (ID) examples.
TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases
Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios.
Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers
This paper presents ReasonFormer, a unified reasoning framework for mirroring the modular and compositional reasoning process of humans in complex decision-making.
Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model
The task divides into two steps: subject suggestion, the task of populating the main column; and gap filling, the task of populating the remaining columns.
Decomposed Meta-Learning for Few-Shot Named Entity Recognition
Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples.
Ranked #5 on
Few-shot NER
on Few-NERD (INTRA)
Issues with Entailment-based Zero-shot Text Classification
The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target label into a sentence of hypothesis and verifying whether or not it could be entailed by the input, aiming at generic classification applicable on any specified label space.
ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering
We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA).
Ranked #1 on
Knowledge Base Question Answering
on GrailQA
CANVASEMB: Learning Layout Representation with Large-scale Pre-training for Graphic Design
Layout representation, which models visual elements in a canvas and their inter-relations, plays a crucial role in graphic design intelligence.
Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments
Large-scale datasets recently proposed for generation contain loosely corresponding data text pairs, where part of spans in text cannot be aligned to its incomplete paired input.
Improving Entity Linking by Modeling Latent Entity Type Information
Existing state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility.
Ranked #2 on
Entity Disambiguation
on AIDA-CoNLL
(Micro-F1 metric)
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER).
Ranked #1 on
Cross-Lingual NER
on MSRA
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge
Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge.
An Encoder with non-Sequential Dependency for Neural Data-to-Text Generation
Data-to-text generation aims to generate descriptions given a structured input data (i. e., a table with multiple records).
Measuring Numerical Common Sense: Is A Word Embedding Approach Effective?
To this end, we first used a crowdsourcing service to obtain sufficient data for a subjective agreement on numerical common sense.
Towards Improving Neural Named Entity Recognition with Gazetteers
Most of the recently proposed neural models for named entity recognition have been purely data-driven, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features.
Ranked #14 on
Named Entity Recognition (NER)
on Ontonotes v5 (English)
(using extra training data)
A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation
Recent neural language generation systems often \textit{hallucinate} contents (i. e., producing irrelevant or contradicted facts), especially when trained on loosely corresponding pairs of the input structure and text.
Towards Time-Aware Distant Supervision for Relation Extraction
Time-DS is composed of a time series instance-popularity and two strategies.
Data2Text Studio: Automated Text Generation from Structured Data
Data2Text Studio is a platform for automated text generation from structured data.
Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data
Previous work on grounded language learning did not fully capture the semantics underlying the correspondences between structured world state representations and texts, especially those between numerical values and lexical terms.
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation
Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data.
Aggregated Semantic Matching for Short Text Entity Linking
The task of entity linking aims to identify concepts mentioned in a text fragments and link them to a reference knowledge base.
Operations Guided Neural Networks for High Fidelity Data-To-Text Generation
Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data.
Incorporating Consistency Verification into Neural Data-to-Document Generation
Recent neural models for data-to-document generation have achieved remarkable progress in producing fluent and informative texts.
Neural Math Word Problem Solver with Reinforcement Learning
Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.
Using Intermediate Representations to Solve Math Word Problems
To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system.
A Statistical Framework for Product Description Generation
We present in this paper a statistical framework that generates accurate and fluent product description from product attributes.
Learning Fine-Grained Expressions to Solve Math Word Problems
This method learns the mappings between math concept phrases in math word problems and their math expressions from training data.
List-only Entity Linking
Traditional Entity Linking (EL) technologies rely on rich structures and properties in the target knowledge base (KB).
Trust, but Verify! Better Entity Linking through Automatic Verification
We introduce automatic verification as a post-processing step for entity linking (EL).
