Search Results for author: Siffi Singh

Found 7 papers, 3 papers with code

A Relation Extraction Dataset for Knowledge Extraction from Web Tables

1 code implementation COLING 2022 Siffi Singh, Alham Fikri Aji, Gaurav Singh, Christos Christodoulopoulos

Most datasets are constructed using synthetic tables that lack valuable metadata information, or are limited in size to be considered as a challenging evaluation set.

Knowledge Graphs Relation +1

Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders

no code implementations7 Mar 2024 Yuwei Zhang, Siffi Singh, Sailik Sengupta, Igor Shalyminov, Hang Su, Hwanjun Song, Saab Mansour

The triplet task gauges the model's understanding of two semantic concepts paramount in real-world conversational systems-- negation and implicature.

Clustering intent-classification +2

MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets

no code implementations5 Mar 2024 Hossein Aboutalebi, Hwanjun Song, Yusheng Xie, Arshit Gupta, Justin Sun, Hang Su, Igor Shalyminov, Nikolaos Pappas, Siffi Singh, Saab Mansour

Development of multimodal interactive systems is hindered by the lack of rich, multimodal (text, images) conversational data, which is needed in large quantities for LLMs.

Image-text matching Retrieval +1

TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization

1 code implementation20 Feb 2024 Liyan Tang, Igor Shalyminov, Amy Wing-mei Wong, Jon Burnsky, Jake W. Vincent, Yu'an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, Kathleen McKeown

We find that there are diverse errors and error distributions in model-generated summaries and that non-LLM based metrics can capture all error types better than LLM-based evaluators.

Hallucination News Summarization +2

Enhancing Abstractiveness of Summarization Models through Calibrated Distillation

no code implementations20 Oct 2023 Hwanjun Song, Igor Shalyminov, Hang Su, Siffi Singh, Kaisheng Yao, Saab Mansour

Our experiments show that DisCal outperforms prior methods in abstractive summarization distillation, producing highly abstractive and informative summaries.

Abstractive Text Summarization Informativeness +1

SWING: Balancing Coverage and Faithfulness for Dialogue Summarization

1 code implementation25 Jan 2023 Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma, Wei Xiao, Feng Nan, Nicholas Dingwall, William Yang Wang, Kathleen McKeown

Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries.

Natural Language Inference

Relation Extraction from Tables using Artificially Generated Metadata

no code implementations24 Aug 2021 Gaurav Singh, Siffi Singh, Joshua Wong, Amir Saffari

To address this issue, we propose methods to artificially create some of this metadata for synthetic tables.

Relation Relation Extraction

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