Papers by Sarik Ghazarian
What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation (2022.findings-acl)
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| Challenge: | Existing metrics for dialog evaluation are trained on human annotations, which is cumbersome to collect. |
| Approach: | They propose to use user sentiment and other information as proxy to measure the quality of previous dialogs. |
| Outcome: | The proposed model is comparable to models trained on human annotated data. |
ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems (2023.acl-long)
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| Challenge: | evaluating commonsense in dialogue systems remains an open challenge . despite the success of open-domain dialogue systems, systems struggle to produce commonsensical responses as humans do. |
| Approach: | They propose an event commonsense evaluation metric empowered by commonsensence knowledge bases. |
| Outcome: | The proposed metric achieves higher correlations with human judgments than baselines. |
ParsiNLU: A Suite of Language Understanding Challenges for Persian (2021.tacl-1)
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Daniel Khashabi, Arman Cohan, Siamak Shakeri, Pedram Hosseini, Pouya Pezeshkpour, Malihe Alikhani, Moin Aminnaseri, Marzieh Bitaab, Faeze Brahman, Sarik Ghazarian, Mozhdeh Gheini, Arman Kabiri, Rabeeh Karimi Mahabagdi, Omid Memarrast, Ahmadreza Mosallanezhad, Erfan Noury, Shahab Raji, Mohammad Sadegh Rasooli, Sepideh Sadeghi, Erfan Sadeqi Azer, Niloofar Safi Samghabadi, Mahsa Shafaei, Saber Sheybani, Ali Tazarv, Yadollah Yaghoobzadeh
| Challenge: | Despite progress in natural language understanding, most progress is concentrated on resource-rich languages like English . despite high-quality benchmarks, there are few available NLU datasets for Persian language . |
| Approach: | They propose a benchmark for Persian language that includes a range of language understanding tasks . they present their results on monolingual and multilingual pre-trained language models . |
| Outcome: | The proposed benchmarks compare human performance with monolingual and multilingual models on Persian language with high quality evaluation datasets. |
DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation (2021.naacl-demos)
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| Challenge: | Recent advances in transformer-based language models and their applications to dialogue systems have succeeded to generate fluent and human-like responses. |
| Approach: | They propose to leverage conversational lines as controllable and informative content-planning elements to guide the generation model produce engaging and informative responses. |
| Outcome: | The proposed system generates engaging and informative responses using convlines as controllable and informative content-planning elements. |
Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation (2021.naacl-main)
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| Challenge: | Existing methods to generate implausible stories using plots are unnatural and oversimplify the characteristics of implusible machine-generated stories. |
| Approach: | They propose to generate a more comprehensive set of implausible stories using plots . plots are structured representations of controllable factors used to generate stories . |
| Outcome: | The proposed model improves the quality of generated implausible stories using plots . it shows that the evaluation metrics trained on the generated data correlate better with human judgments compared to baselines. |
AXCEL: Automated eXplainable Consistency Evaluation using LLMs (2024.findings-emnlp)
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P Aditya Sreekar, Sahil Verma, Suransh Chopra, Abhishek Persad, Sarik Ghazarian, Narayanan Sadagopan
| Challenge: | Large Language Models (LLMs) are widely used for various tasks but evaluating the consistency of generated text remains a challenge. |
| Approach: | They propose a prompt-based consistency metric which provides explanations for consistency scores by providing detailed reasoning and pinpointing inconsistent text spans. |
| Outcome: | The proposed metric outperforms state-of-the-art metrics in summarization, free text generation and data-to-text conversion tasks by 8.7% and 6.2%. |
DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations (2022.acl-long)
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| Challenge: | Existing evaluation metrics for incoherent dialogues are insufficient to accurately reflect incohérence . despite the effectiveness of large pretrained language models, not everyone is into this type of work. |
| Approach: | They propose a Dialogue coherence Evaluation metric that uses Abstract Meaning Representation to apply semantic-level Manipulations for incoherent (negative) data generation. |
| Outcome: | The proposed evaluation metric achieves higher correlations with human judgments compared to baseline methods on dialog datasets by significant margins. |