Papers by Malihe Alikhani

How to Align Multiple Signed Language Corpora for Better Sign-to-Sign Translations? (2025.naacl-long)

Challenge:	Enthusiasm is an important part of engaging communication.
Approach:	They propose a multimodal dataset for studying enthusiasm composed of video, audio, and text.
Outcome:	The proposed model shows that pitch, loudness, and discourse relation parsing are important in distinguishing enthusiastic communication.

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Mert Inan, Yang Zhong, Vidya Ganesh, Malihe Alikhani

Multilinguality

Challenge:	despite the growing need for advanced signing technologies, signed language resources remain scarce.
Approach:	They propose a linguistically informed alignment algorithm that matches instances between signed languages . they compare similarities and differences across three signed languages to develop a model .
Outcome:	The proposed algorithm performs well on automatic metrics for sign-to-sign translation and generation.

Arrows are the Verbs of Diagrams (C18-1)

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Malihe Alikhani, Matthew Stone

Challenge:	arrows are a key ingredient of schematic pictorial communication.
Approach:	They propose a linguistically-inspired approach to the interpretation of arrows using existing linguistic concepts and methodologies.
Outcome:	The proposed approach is based on linguistic, crowdsourcing and machine-learning experiments.

Measuring Bias and Agreement in Large Language Model Presupposition Judgments (2025.findings-acl)

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Katherine Atwell, Mandy Simons, Malihe Alikhani

Accounting for Sycophancy in Language Model Uncertainty Estimation (2025.findings-naacl)

Challenge:	Identifying linguistic bias in text requires the identification of explicit statements and presuppositions . large language models can be used to detect subtle forms of bias with no clear lexical signals .
Approach:	They propose to prompt large language models to evaluate presuppositions across texts . they find that LLMs may inadvertently reflect societal biases when identifying presuposed content .
Outcome:	The proposed model can be used to detect linguistic biases in text, but its accuracy is unclear . linguistic factors associated with human-model alignment suggest biase influenced by gender and ideology.

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Anthony Sicilia, Mert Inan, Malihe Alikhani

SignAlignLM: Integrating Multimodal Sign Language Processing into Large Language Models (2025.findings-acl)

Challenge:	Effective human-machine collaboration requires machine learning models to externalize uncertainty.
Approach:	They propose a generalization of the definition of sycophancy bias and a new algorithm to account for scophancies in uncertainty estimation.
Outcome:	The proposed algorithm can account for sycophancy in uncertainty estimation process.

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Mert Inan, Anthony Sicilia, Malihe Alikhani

Language Models

Challenge:	Deaf and Hard-of-Hearing (DHH) users increasingly utilize Large Language Models (LLMs), yet face significant challenges due to these models’ limited understanding of sign language grammar, multimodal sign inputs, and Deafic cultural contexts.
Approach:	They propose to use sign language support in LLMs to integrate sign linguistic rules and conventions into prompting and fine-tuning strategies to address the needs of DHH users.
Outcome:	The proposed model can be generalized interfaces for both spoken and signed languages if trained with a multitasking paradigm.

Identifying & Interactively Refining Ambiguous User Goals for Data Visualization Code Generation (2025.emnlp-main)

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Mert Inan, Anthony Sicilia, Alex Xie, Saujas Vaduguru, Daniel Fried, Malihe Alikhani

ParsiNLU: A Suite of Language Understanding Challenges for Persian (2021.tacl-1)

Challenge:	ambiguities in natural language can lead to outputs that seem correct but fail to reflect the speaker’s intent.
Approach:	They propose to identify and then resolve ambiguities in natural language and propose metrics to quantify them.
Outcome:	The proposed metrics better correlate with human annotations than uncertainty baselines.

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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.

COSMic: A Coherence-Aware Generation Metric for Image Descriptions (2021.findings-emnlp)

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Mert Inan, Piyush Sharma, Baber Khalid, Radu Soricut, Matthew Stone, Malihe Alikhani

Evaluating Theory of (an uncertain) Mind: Predicting the Uncertain Beliefs of Others from Conversational Cues (2025.acl-long)

Challenge:	Existing methods to evaluate captions have limited learning of their output . previous methods focused on n-gram measures of similarity to reference output based on a ngram of similarities to the output metric.
Approach:	They propose a first discourse-aware learned generation metric for evaluating image descriptions.
Outcome:	The proposed metric predicts human ratings of captions on out-of-domain images.

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CITE: A Corpus of Image-Text Discourse Relations (N19-1)

Challenge:	Typically, beliefs are held or not held, but there are situations where an individual's beliefs are better represented more flexibly.
Approach:	They propose a set of tasks that challenge language models to model the uncertainty of participants in a dialogue.
Outcome:	The proposed tasks show that language models can model the uncertainty of participants in a conversation.

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Malihe Alikhani, Sreyasi Nag Chowdhury, Gerard de Melo, Matthew Stone

APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations (2022.coling-1)

Challenge:	a crowd-sourced resource characterizes inferences in image-text contexts in the domain of cooking recipes . a recent study has found that image-image presentations are more effective at integrating text and image .
Approach:	They propose a crowd-sourced resource for multimodal discourse characterizing inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations.
Outcome:	The proposed corpus enables a better understanding of communication and common-sense reasoning . it is particularly important for automating the understanding and generation of text-image presentations .

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Katherine Atwell, Sabit Hassan, Malihe Alikhani

Ethical NLP

Challenge:	Using style-transfer models to reduce offensiveness of social media comments is difficult because of limited labeled data.
Approach:	They propose two methods to integrate discourse relations with pretrained style-transfer models and evaluate them on a reddit dataset.
Outcome:	The proposed models can reduce offensiveness while preserving original meaning . they are the first to examine inferential links between comment and original text .

Multilingual Content Moderation: A Case Study on Reddit (2023.eacl-main)

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Meng Ye, Karan Sikka, Katherine Atwell, Sabit Hassan, Ajay Divakaran, Malihe Alikhani

Multilinguality

Challenge:	a growing need for AI moderators to safeguard users and protect mental health of human moderator from traumatic content.
Approach:	They propose to use a multilingual dataset to study the challenges of content moderation . they propose to analyze 1.8 million Reddit comments in English, german, spanish and french .
Outcome:	The proposed dataset highlights the challenges and suggests related research problems . it shows that the proposed model can be used to predict the violated rule .

Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI (2025.coling-industry)

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Yuya Asano, Sabit Hassan, Paras Sharma, Anthony B. Sicilia, Katherine Atwell, Diane Litman, Malihe Alikhani

Speech Recognition

Challenge:	Existing ASR correction methods rely on prior user data or named entities . Existing methods based on prior data are not available for goal-oriented dialogues .
Approach:	They propose a method that integrates contextual information from the dialogue states of a goal-oriented conversational AI and its tasks into a large language model.
Outcome:	The proposed method improves recall and F1 of correction by 34% and 16% while maintaining precision and false positive rate.

Dialogue is the Plan: From Interface to Joint Action in Agentic AI (2026.acl-short)

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Mert Inan, Malihe Alikhani, Anthony Sicilia

Achieving Common Ground in Multi-modal Dialogue (2020.acl-tutorials)

Challenge:	Large Language Model agents' language use is often used as an interface for instructing and reporting results.
Approach:	They argue that large language models are often used as an interface for instructingactions and reporting results.
Outcome:	We show that large-scale language models can be used to plan and act, yet their language is often used as an interface for instructing and reporting results.

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Malihe Alikhani, Matthew Stone

The Role of Context and Uncertainty in Shallow Discourse Parsing (2022.coling-1)

Challenge:	tutorial focuses on three main topic areas: grounding in human-human communication, dialogue systems and multi-modal interactive systems.
Approach:	This tutorial examines the use of computational dialogue research to design grounding modules and behaviors in cutting-edge systems.
Outcome:	This tutorial examines the results of recent research on grounding in human-human communication . it shows how these results lead to rich and challenging opportunities for doing grounding more flexible and powerful ways .

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Katherine Atwell, Remi Choi, Junyi Jessy Li, Malihe Alikhani

OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation (2026.acl-long)

Challenge:	Discourse parsing has proven to be useful for a number of NLP tasks that require complex reasoning.
Approach:	They hypothesize that context plays an important role in accurate human annotation and add uncertainty measures can improve model accuracy and calibration.
Outcome:	The proposed model can be better calibrated by adding uncertainty measures to models with better accuracy and calibration.

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Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini, Bo Sun, Yakov Bart, Weimin Lyu, Jiri Gesi, Tian Wang, Jing Huang, Yu Su, Upol Ehsan, Malihe Alikhani, Toby Jia-Jun Li, Lydia Chilton, Dakuo Wang

Challenge:	evaluating LLMs' ability to mimic real user behavior remains an open challenge due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual user.
Approach:	They propose a dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions.
Outcome:	The proposed dataset is the first to evaluate how well current LLMs can accurately simulate the next web action of a specific user.

Combining Discourse Coherence with Large Language Models for More Inclusive, Equitable, and Robust Task-Oriented Dialogue (2024.lrec-main)

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Katherine Atwell, Mert Inan, Anthony B. Sicilia, Malihe Alikhani

LEATHER: A Framework for Learning to Generate Human-like Text in Dialogue (2022.findings-aacl)

Challenge:	Large language models (LLMs) are capable of generating well-formed responses, but they struggle in goal-oriented settings.
Approach:	They propose a discourse-aware multimodal task-oriented dialogue system that combines discourse theories with offline LLM generation.
Outcome:	The proposed system reduces misunderstandings in the dialect of African-American Vernacular English from 93% to 57%.

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Modeling Non-Cooperative Dialogue: Theoretical and Empirical Insights (2022.tacl-1)

Challenge:	Generating coherent, human-like text for dialogue remains a challenge . lack of careful design of rewards can lead to mode-collapse in dialogue .
Approach:	They propose a theoretical framework for learning to generate text in dialogue . they propose to use data-shift to develop theoretical guarantees for learners .
Outcome:	The proposed framework improves both task-success and human-likeness of the generated text.

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Anthony Sicilia, Tristan Maidment, Pat Healy, Malihe Alikhani

Linguistic Theories

Challenge:	a robust dialogue agent cannot assume a cooperative conversational counterpart when deployed in the wild.
Approach:	They propose a theoretical model for identifying non-cooperative interlocutors . they use reinforcement learning to implement multiple communication strategies .
Outcome:	The proposed model is validated by using reinforcement learning to implement multiple communication strategies.

SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization (2023.emnlp-main)

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Hyunwoo Kim, Jack Hessel, Liwei Jiang, Peter West, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap, Yejin Choi

Natural Language Interfaces

Challenge:	a dataset of 1.5 million conversations distilled from everyday spoken situations is limited in scale due to its associated costs.
Approach:	They propose to make SODA a publicly available, million-scale high-quality social dialogue dataset . they contextualize social commonsense knowledge from a knowledge graph to distill broad spectrum of social interactions .
Outcome:	The proposed dataset is the first publicly available, million-scale high-quality social dialogue dataset.

Political Ideology and Polarization: A Multi-dimensional Approach (2022.naacl-main)

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Barea Sinno, Bernardo Oviedo, Katherine Atwell, Malihe Alikhani, Junyi Jessy Li

Sentiment Analysis

Challenge:	Recent research has made great strides towards understanding the ideological bias (i.e., stance) of news media along the left-right spectrum.
Approach:	They propose a novel approach for the study of ideology based on its left or right positions on the issue being discussed.
Outcome:	The proposed method allows for the quantitative and temporal measurement and analysis of polarization as a multidimensional ideological distance.

Aspectuality Across Genre: A Distributional Semantics Approach (2020.coling-main)

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Thomas Kober, Malihe Alikhani, Matthew Stone, Mark Steedman

Challenge:	Existing studies have focused on the aspectual class of verbs in English for predicting coherence relations in text and imagery, predicting links in entailment graphs and interpreting sign languages.
Approach:	They propose to model two elementary aspects of aspectual class, states vs. events, and telic v. atelic events, with distributional semantics.
Outcome:	The proposed model outperforms previous work on three datasets and shows that telicity correlates with genre and discourse goals.

Cross-modal Coherence Modeling for Caption Generation (2020.acl-main)

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Malihe Alikhani, Piyush Sharma, Shengjie Li, Radu Soricut, Matthew Stone

Studying and Mitigating Biases in Sign Language Understanding Models (2024.emnlp-main)

Challenge:	Existing methods for image captioning do not guarantee consistent image-text relations . current models do not provide enough data for training robust captioning models .
Approach:	They use an annotation protocol specifically devised for capturing image–caption coherence relations to study image captioning.
Outcome:	The proposed protocol improves image captioning models with coherence relations . the dataset is large enough to alleviate content hallucinations, the authors show .

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Katherine Atwell, Danielle Bragg, Malihe Alikhani

PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification Data for Learning Enhanced Generation (2022.coling-1)

Challenge:	Using crowd-sourced sign language datasets to reduce performance disparities is critical to addressing potential biases and inequities.
Approach:	They use demographic information to study biases that may result from models trained on crowd-sourced sign datasets.
Outcome:	The proposed approach reduces performance disparities without decreasing accuracy.

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Sedrick Scott Keh, Kevin Lu, Varun Gangal, Steven Y. Feng, Harsh Jhamtani, Malihe Alikhani, Eduard Hovy

Challenge:	Personifications are figures of speech that endow inanimate entities with properties and actions typically seen as requiring animacy.
Approach:	They propose to use personification data to train a parallel corpus of personifications . they propose to combine personification-related literalizations with automatic ones .
Outcome:	The proposed personification system can generate diverse and creative personifications . it can generate personification-related qualities such as interestingness and animacy .

Including Signed Languages in Natural Language Processing (2021.acl-long)

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Kayo Yin, Amit Moryossef, Julie Hochgesang, Yoav Goldberg, Malihe Alikhani

Challenge:	Existing research in Sign Language Processing (SLP) rarely explores signed languages . authors urge adoption of an efficient tokenization method and the collection of real-world signed language data .
Approach:	They propose to include signed languages as a research area with high social and scientific impact . they review the limitations of current SLP models and identify the open challenges .
Outcome:	The proposed model should include signed languages as a research area with high social and scientific impact.

Modeling Intensification for Sign Language Generation: A Computational Approach (2022.findings-acl)

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Mert Inan, Yang Zhong, Sabit Hassan, Lorna Quandt, Malihe Alikhani

Text Generation

Challenge:	End-to-end sign language generation models do not accurately represent prosody in sign language.
Approach:	They propose to model intensification in a data-driven manner to improve prosody in generated sign languages by modeling temporal and spatial variations.
Outcome:	The proposed models improve the prosody of generated sign languages by using data-driven models.

MedNgage: A Dataset for Understanding Engagement in Patient-Nurse Conversations (2023.findings-acl)

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Yan Wang, Heidi Donovan, Sabit Hassan, Malihe Alikhani

PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically (2023.eacl-main)

Challenge:	Literature suggests that actively engaged patients are more likely to obtain the full benefits of an intervention and exhibit better outcomes.
Approach:	They propose to annotate a dataset of patient-nurse conversations about cancer symptom management using a new framework for patient engagement.
Outcome:	The proposed model predicts patient-nurse conversations from socio-affective and cognitive dimensions.

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Sedrick Scott Keh, Steven Y. Feng, Varun Gangal, Malihe Alikhani, Eduard Hovy

Syntactic Text Processing

Challenge:	Phonetic difficulty is hard to characterize and can be expressed in tongue twisters through alliteration and homophony.
Approach:	They propose a phoneme-aware neural completion to generate tongue twisters automatically . they leverage phoneme representations to capture phonetic difficulty and train language models .
Outcome:	The proposed language model generates novel, phonetically difficult, fluent, and semantically meaningful tongue twisters on two task settings.

Adaptive Platt Scaling with Causal Interpretations for Self-Reflective Language Model Uncertainty Estimates (2025.findings-emnlp)

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The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error (2022.findings-acl)

Challenge:	a new study examines the ability of large language models to self-monitor and ask for human intervention.
Approach:	They propose a formal analysis of LLM self-reflection for uncertainty estimation using domain adaptation theory.
Outcome:	The proposed method improves accuracy and human interpretation on reasoning tasks.

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Katherine Atwell, Anthony Sicilia, Seong Jae Hwang, Malihe Alikhani

Signed Coreference Resolution (2021.emnlp-main)

Challenge:	Discourse analysis is very low on texts outside of the training distribution’s coverage, diminishing the practical utility of existing models.
Approach:	They propose to use a distribution shift statistic to estimate the error-gap of a discourse model and to use it to estimate it.
Outcome:	The proposed model can be estimated via distribution shift but does not correlate with change in the observed error of a classifier (i.e. error-gap).

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Kayo Yin, Kenneth DeHaan, Malihe Alikhani

Information Extraction & Text Mining

Challenge:	Sign Language Processing is based on linguistic theories of spoken languages and expect either speech or written text as input.
Approach:	They propose a new challenge for coreference modeling and Sign Language Processing to solve this problem.
Outcome:	The proposed models will be linguistically informed and can address the complexities of the challenge effectively.

Seeing Eye-to-Eye: Cross-Modal Coherence Relations Inform Eye-gaze Patterns During Comprehension & Production (2024.lrec-main)

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Mert Inan, Malihe Alikhani

Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue (2020.coling-main)

Challenge:	Xu and Stone et al., 2014, show eye movements are correlated with discourse goals but the relationship between eye movements and coherence is a missing link.
Approach:	They propose an eye gaze pattern ranking algorithm and a semantic gaze visualization technique to study eye gaze patterns and coherence relations in multimodal language contexts.
Outcome:	The proposed method combines eye-tracking and a semantic gaze visualization technique to study eye movements in multimodal language contexts.

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Baber Khalid, Malihe Alikhani, Matthew Stone

An Active Learning Framework for Inclusive Generation by Large Language Models (2025.coling-main)

Challenge:	In many domains, dialogue systems need to work collaboratively with users to reconstruct meaning . this requires a system that can give targeted, effective feedback about the system’s understanding .
Approach:	They propose a system that collaborates on reference tasks that distinguish arbitrarily varying color patches from similar distractors and use crowd workers to test their approach.
Outcome:	The proposed system can distinguish varying color patches from distractors and elicit correct answers that the system understands.

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Sabit Hassan, Anthony B. Sicilia, Malihe Alikhani

How people talk about each other: Modeling Generalized Intergroup Bias and Emotion (2023.eacl-main)

Challenge:	Large Language Models (LLMs) exhibit bias toward underrepresented groups, despite advances in active learning.
Approach:	They propose a clustering-based active learning framework enhanced with knowledge distillation that transforms the intermediate outputs of the learner model to yield more representative models without prior knowledge of underlying data distribution.
Outcome:	The proposed framework improves performance across data subgroups and lexical diversity, underscoring the model’s resilience to skewness in available data.

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Venkata Subrahmanyan Govindarajan, Katherine Atwell, Barea Sinno, Malihe Alikhani, David I. Beaver, Junyi Jessy Li

Learning to Generate Equitable Text in Dialogue from Biased Training Data (2023.acl-long)

Challenge:	Current studies of bias in NLP rely on identifying (unwanted or negative) bias towards a specific demographic group, but this is not always practical.
Approach:	They extrapolate a notion of bias from social science literature to predict interpersonal group relationship (IGR) using interpersonal emotions as an anchor.
Outcome:	The proposed model predicts the interpersonal group relationship (IGR) using interpersonal emotions as an anchor.

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Including Facial Expressions in Contextual Embeddings for Sign Language Generation (2023.starsem-1)

Challenge:	Absence of equitable and inclusive principles can hinder the formation of common ground, which in turn negatively impacts the overall performance of the system.
Approach:	They propose to use theories of computational learning to study equitable text generation in dialogues using augmented data to prove formal definitions of equity in text generation and formal connections between human-likeness and learning equity.
Outcome:	The proposed model predicts relative-performance of multiple algorithms in generating equitable text as measured by human and automated evaluation.

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Carla Viegas, Mert Inan, Lorna Quandt, Malihe Alikhani

Representation Learning

Challenge:	State-of-the-art sign language generation frameworks lack expressivity and naturalness . current systems focus on manual signs, neglecting affective, grammatical and semantic functions of facial expressions . communication between the Deaf and Hard of Hearing (DHH) individuals may be facilitated by emerging language technologies .
Approach:	They propose a Dual Encoder Transformer capable of generating manual signs and facial expressions by capturing similarities and differences found in text and sign gloss annotations.
Outcome:	The proposed model improves the quality of automatically generated sign language.

HumBEL: A Human-in-the-Loop Approach for Evaluating Demographic Factors of Language Models in Human-Machine Conversations (2024.eacl-long)

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Anthony Sicilia, Jennifer Gates, Malihe Alikhani