activity
20182025
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 594 across the 14 of their papers we have counts for

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Showing cs.CLShow all

12 papers · 1 filter

cs.CL2025

TOD-ProcBench: Benchmarking Complex Instruction-Following in Task-Oriented Dialogues

Sarik Ghazarian, Abhinav Gullapalli, Swair Shah +4

In real-world task-oriented dialogue (TOD) settings, agents are required to strictly adhere to complex instructions while conducting multi-turn conversations with customers. These…

cs.CL2023★ 2 cited

Overview of Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems at DSTC 11 Track 4

Mario Rodríguez-Cantelar, Chen Zhang, Chengguang Tang +5

The advent and fast development of neural networks have revolutionized the research on dialogue systems and subsequently have triggered various challenges regarding their automatic…

cs.CL2023

ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems

Sarik Ghazarian, Yijia Shao, Rujun Han +2

Commonsense reasoning is omnipresent in human communications and thus is an important feature for open-domain dialogue systems. However, evaluating commonsense in dialogue systems…

cs.CL2022★ 565 cited

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…

cs.CL2022★ 3 cited

What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation

Sarik Ghazarian, Behnam Hedayatnia, Alexandros Papangelis +2

Accurate automatic evaluation metrics for open-domain dialogs are in high demand. Existing model-based metrics for system response evaluation are trained on human annotated data, w…

cs.CL2022★ 1 cited

DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations

Sarik Ghazarian, Nuan Wen, Aram Galstyan +1

Automatic evaluation metrics are essential for the rapid development of open-domain dialogue systems as they facilitate hyper-parameter tuning and comparison between models. Althou…

cs.CL2021

User Response and Sentiment Prediction for Automatic Dialogue Evaluation

Sarik Ghazarian, Behnam Hedayatnia, Alexandros Papangelis +2

Automatic evaluation is beneficial for open-domain dialog system development. However, standard word-overlap metrics (BLEU, ROUGE) do not correlate well with human judgements of op…

cs.CL2021

Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation

Sarik Ghazarian, Zixi Liu, Akash SM +3

With the recent advances of open-domain story generation, the lack of reliable automatic evaluation metrics becomes an increasingly imperative issue that hinders the fast developme…

cs.CL2021

DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation

Sarik Ghazarian, Zixi Liu, Tuhin Chakrabarty +3

Having engaging and informative conversations with users is the utmost goal for open-domain conversational systems. Recent advances in transformer-based language models and their a…

cs.CL2020★ 7 cited

ParsiNLU: A Suite of Language Understanding Challenges for Persian

Daniel Khashabi, Arman Cohan, Siamak Shakeri +22

Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich l…

cs.CL2019

Predictive Engagement: An Efficient Metric For Automatic Evaluation of Open-Domain Dialogue Systems

Sarik Ghazarian, Ralph Weischedel, Aram Galstyan +1

User engagement is a critical metric for evaluating the quality of open-domain dialogue systems. Prior work has focused on conversation-level engagement by using heuristically cons…

cs.CL2019★ 15 cited

Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings

Sarik Ghazarian, Johnny Tian-Zheng Wei, Aram Galstyan +1

Despite advances in open-domain dialogue systems, automatic evaluation of such systems is still a challenging problem. Traditional reference-based metrics such as BLEU are ineffect…