Papers by Farhad Moghimifar
NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery (2023.findings-acl)
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| Challenge: | Existing methods for norm recognition focus only on surface-level features of dialogues and do not take into account the interactions within a conversation. |
| Approach: | They propose a probabilistic generative Markov model to carry latent features throughout a dialogue and trainable on weakly annotated data using the variational technique. |
| Outcome: | The proposed model outperforms current state-of-the-art methods on a weakly annotated dataset, outperforming existing methods, including GPT3. |
CosMo: Conditional Seq2Seq-based Mixture Model for Zero-Shot Commonsense Question Answering (2020.coling-main)
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| Challenge: | Identifying the implicit causes and effects of a social context is the driving capability of commonsense reasoning. |
| Approach: | They propose a conditional seq2seq-based mixture model which generates context-dependent clauses for commonsense reasoning. |
| Outcome: | The proposed model improves on the current state-of-the-art models by +5.2% over existing models. |
Neural-Symbolic Commonsense Reasoner with Relation Predictors (2021.acl-short)
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| Challenge: | Existing models for commonsense reasoning are limited by their limited set of facts, rendering them unfit for reasoning over new unseen situations and events. |
| Approach: | They propose a neural-symbolic reasoner which can combine commonsense facts with large-scale dynamic CKGs to draw conclusions about ordinary situations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the task of link prediction on CKGs. |
Proverbs Run in Pairs: Evaluating Proverb Translation Capability of Large Language Model (2025.findings-acl)
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| Challenge: | Recent research has demonstrated that large language models (LLMs) can translate cultural elements in languages such as idioms and proverbs. |
| Approach: | They propose to use large language models to translate culturally rooted proverbs in conversation and between languages with similar cultural backgrounds to compare their results. |
| Outcome: | The proposed models can achieve good translation between languages with similar cultural backgrounds and outperform NMT models in proverb translation. |
CLARITY: A Framework and Benchmark for Conversational Language Ambiguity and Unanswerability in Interactive NL2SQL Systems (2026.acl-industry)
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Tabinda Sarwar, Farhad Moghimifar, Cong Duy Vu Hoang, Xiaoxiao Ma, Shawn Chang Xu, Fahimeh Saleh, Poorya Zaremoodi, Avirup Sil, Katrin Kirchhoff
| Challenge: | Existing benchmarks assume a single source of ambiguity and rely on user interaction for resolution, overlooking realistic failure modes. |
| Approach: | They propose a framework for automatically generating an NL2SQL benchmark with multi-faceted ambiguities and diverse user behaviors. |
| Outcome: | The proposed framework transforms executable SQL into ambiguous queries with a conversational continuation and schema-level metadata. |