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Found 20 papers, 8 papers with code
Benchmarking Language-agnostic Intent Classification for Virtual Assistant Platforms
Current virtual assistant (VA) platforms are beholden to the limited number of languages they support.
Do Large Language Models Have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs
Despite the importance of this issue, the naturalness of multilingual LLM outputs has received limited attention.
Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs
Translating text that contains entity names is a challenging task, as cultural-related references can vary significantly across languages.
ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA datasets with Large Language Models
The rapid advancement of Large Language Models (LLMs) and conversational assistants necessitates dynamic, scalable, and configurable conversational datasets for training and evaluation.
AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings
Ranking is a fundamental and popular problem in search.
Entity Disambiguation via Fusion Entity Decoding
Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark.
Ranked #1 on
Entity Linking
on KORE50
(Micro-F1 strong metric)
Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants
Out of Scope (OOS) detection in Conversational AI solutions enables a chatbot to handle a conversation gracefully when it is unable to make sense of the end-user query.
Fast and Light-Weight Answer Text Retrieval in Dialogue Systems
Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available.
Improved Text Classification via Contrastive Adversarial Training
We propose a simple and general method to regularize the fine-tuning of Transformer-based encoders for text classification tasks.
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
Recent advancements in open-domain question answering (ODQA), i. e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets.
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations
Secondly, even with large training data, the intent detection models can see a different distribution of test data when being deployed in the real world, leading to poor accuracy.
Multilingual BERT Post-Pretraining Alignment
We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved zero-shot cross-lingual transferability of the pretrained models.
Frustratingly Hard Evidence Retrieval for QA Over Books
A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth.
Context-Aware Conversation Thread Detection in Multi-Party Chat
In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs.
Out-of-Domain Detection for Low-Resource Text Classification Tasks
Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task.
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
Most approaches to extraction multiple relations from a paragraph require multiple passes over the paragraph.
Ranked #19 on
Relation Extraction
on SemEval-2010 Task-8
Diverse Few-Shot Text Classification with Multiple Metrics
We study few-shot learning in natural language domains.
Robust Task Clustering for Deep Many-Task Learning
We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario.
Neural Models for Sequence Chunking
Many natural language understanding (NLU) tasks, such as shallow parsing (i. e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence.
