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Found 24 papers, 9 papers with code
Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization
In this article, we address this challenge by exploring a series of domain adaptation techniques.
Query-OPT: Optimizing Inference of Large Language Models via Multi-Query Instructions in Meeting Summarization
This work focuses on the task of query-based meeting summarization in which the summary of a context (meeting transcript) is generated in response to a specific query.
Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges
Recent years have witnessed a substantial increase in the use of deep learning to solve various natural language processing (NLP) problems.
Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?
In this paper, we investigate whether smaller, compact LLMs are a good alternative to the comparatively Larger LLMs2 to address significant costs associated with utilizing LLMs in the real world.
Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs
In recent years, Large Language Models (LLMs) have gained immense attention due to their notable emergent capabilities, surpassing those seen in earlier language models.
Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective
This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs).
A Comprehensive Evaluation of Large Language Models on Benchmark Biomedical Text Processing Tasks
While their performance is still quite poor in comparison to the biomedical models that were fine-tuned on large training sets, our findings demonstrate that LLMs have the potential to be a valuable tool for various biomedical tasks that lack large annotated data.
BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP
Large Language Models (LLMs) have emerged as one of the most important breakthroughs in NLP for their impressive skills in language generation and other language-specific tasks.
Abstractive Text Summarization
Natural Language Inference
+6
Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers
ChatGPT is a large language model developed by OpenAI.
A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets
The development of large language models (LLMs) such as ChatGPT has brought a lot of attention recently.
Ranked #8 on
Natural Language Inference
on ANLI test
AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching
In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise.
ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries
Automatic chart to text summarization is an effective tool for the visually impaired people along with providing precise insights of tabular data in natural language to the user.
CQSumDP: A ChatGPT-Annotated Resource for Query-Focused Abstractive Summarization Based on Debatepedia
Debatepedia is a publicly available dataset consisting of arguments and counter-arguments on controversial topics that has been widely used for the single-document query-focused abstractive summarization task in recent years.
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop
Telephone transcription data can be very noisy due to speech recognition errors, disfluencies, etc.
Entity-level Sentiment Analysis in Contact Center Telephone Conversations
Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text.
DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities
Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data.
An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts
We present a simple yet effective method to train a named entity recognition (NER) model that operates on business telephone conversation transcripts that contain noise due to the nature of spoken conversation and artifacts of automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations
An Entity Linking system aligns the textual mentions of entities in a text to their corresponding entries in a knowledge base.
Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization
In this paper, we address this challenge by exploring a series of domain adaptation techniques.
Improving Punctuation Restoration for Speech Transcripts via External Data
To leverage the available written text datasets, we introduce a data sampling technique based on an n-gram language model to sample more training data that are similar to our in-domain data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means
Moreover, we evaluate our proposed model on the live streaming data and find that our proposed system can be used for real-time anomaly detection in the industrial setup.
Utilizing Bidirectional Encoder Representations from Transformers for Answer Selection
We find that fine-tuning the BERT model for the answer selection task is very effective and observe a maximum improvement of 13. 1% in the QA datasets and 18. 7% in the CQA datasets compared to the previous state-of-the-art.
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization
In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on the given query.
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task
In this paper, we utilize contextualized word embeddings with the transformer encoder for sentence similarity modeling in the answer selection task.
