no code implementations • SIGDIAL (ACL) 2021 • Katherine Atwell, Junyi Jessy Li, Malihe Alikhani
Discourse parsers recognize the intentional and inferential relationships that organize extended texts.
1 code implementation • NAACL 2022 • Barea Sinno, Bernardo Oviedo, Katherine Atwell, Malihe Alikhani, Junyi Jessy Li
Analyzing ideology and polarization is of critical importance in advancing our grasp of modern politics.
no code implementations • COLING 2022 • Katherine Atwell, Remi Choi, Junyi Jessy Li, Malihe Alikhani
We find that including annotator accuracy and confidence improves model accuracy, and incorporating confidence in the model’s temperature function can lead to models with significantly better-calibrated confidence measures.
1 code implementation • EMNLP 2021 • Kayo Yin, Kenneth DeHaan, Malihe Alikhani
Coreference resolution is key to many natural language processing tasks and yet has been relatively unexplored in Sign Language Processing.
no code implementations • 1 Apr 2024 • Casey Kennington, Malihe Alikhani, Heather Pon-Barry, Katherine Atwell, Yonatan Bisk, Daniel Fried, Felix Gervits, Zhao Han, Mert Inan, Michael Johnston, Raj Korpan, Diane Litman, Matthew Marge, Cynthia Matuszek, Ross Mead, Shiwali Mohan, Raymond Mooney, Natalie Parde, Jivko Sinapov, Angela Stewart, Matthew Stone, Stefanie Tellex, Tom Williams
The ability to interact with machines using natural human language is becoming not just commonplace, but expected.
1 code implementation • 13 Feb 2024 • Maneesh Bilalpur, Mert Inan, Dorsa Zeinali, Jeffrey F. Cohn, Malihe Alikhani
To improve the rapport-building capabilities of embodied agents we annotated backchannel smiles in videos of intimate face-to-face conversations over topics such as mental health, illness, and relationships.
no code implementations • 5 Feb 2024 • Anthony Sicilia, Hyunwoo Kim, Khyathi Raghavi Chandu, Malihe Alikhani, Jack Hessel
Effective interlocutors account for the uncertain goals, beliefs, and emotions of others.
1 code implementation • 29 Nov 2023 • Sabit Hassan, Malihe Alikhani
In this work, we propose a novel framework based on theories of discourse to study the inferential links that connect counter speeches to the hateful comment.
1 code implementation • 10 Jul 2023 • Anthony Sicilia, Malihe Alikhani
Absence of equitable and inclusive principles can hinder the formation of common ground, which in turn negatively impacts the overall performance of the system.
no code implementations • 26 May 2023 • Sabit Hassan, Malihe Alikhani
While active learning (AL) has shown promise in training models with a small amount of annotated data, AL's reliance on the model's behavior for selective sampling can lead to an accumulation of unwanted bias rather than bias mitigation.
1 code implementation • 23 May 2023 • Anthony Sicilia, Jennifer C. Gates, Malihe Alikhani
While demographic factors like age and gender change the way people talk, and in particular, the way people talk to machines, there is little investigation into how large pre-trained language models (LMs) can adapt to these changes.
1 code implementation • 19 Feb 2023 • Meng Ye, Karan Sikka, Katherine Atwell, Sabit Hassan, Ajay Divakaran, Malihe Alikhani
Content moderation is the process of flagging content based on pre-defined platform rules.
1 code implementation • 20 Dec 2022 • Hyunwoo Kim, Jack Hessel, Liwei Jiang, Peter West, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Le Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap, Yejin Choi
Data scarcity has been a long standing issue in the field of open-domain social dialogue.
3 code implementations • 14 Oct 2022 • Anthony Sicilia, Malihe Alikhani
From this insight, we propose a new algorithm, and empirically, we demonstrate our proposal improves both task-success and human-likeness of the generated text.
1 code implementation • COLING 2022 • Katherine Atwell, Sabit Hassan, Malihe Alikhani
Then, we introduce the first discourse-aware style-transfer models that can effectively reduce offensiveness in Reddit text while preserving the meaning of the original text.
1 code implementation • COLING 2022 • Sedrick Scott Keh, Kevin Lu, Varun Gangal, Steven Y. Feng, Harsh Jhamtani, Malihe Alikhani, Eduard Hovy
To this end, we propose PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation.
2 code implementations • 14 Sep 2022 • Venkata S Govindarajan, Katherine Atwell, Barea Sinno, Malihe Alikhani, David I. Beaver, Junyi Jessy Li
Current studies of bias in NLP rely mainly on identifying (unwanted or negative) bias towards a specific demographic group.
no code implementations • 13 Sep 2022 • Sedrick Scott Keh, Steven Y. Feng, Varun Gangal, Malihe Alikhani, Eduard Hovy
Through automatic and human evaluation, as well as qualitative analysis, we show that PANCETTA generates novel, phonetically difficult, fluent, and semantically meaningful tongue twisters.
1 code implementation • 15 Jul 2022 • Anthony Sicilia, Tristan Maidment, Pat Healy, Malihe Alikhani
We use the tools of learning theory to develop a theoretical model for identifying non-cooperative interlocutors and apply this theory to analyze different communication strategies.
1 code implementation • 12 Jul 2022 • Anthony Sicilia, Katherine Atwell, Malihe Alikhani, Seong Jae Hwang
Multiclass neural networks are a common tool in modern unsupervised domain adaptation, yet an appropriate theoretical description for their non-uniform sample complexity is lacking in the adaptation literature.
no code implementations • 5 Jul 2022 • Malihe Alikhani, Thomas Kober, Bashar Alhafni, Yue Chen, Mert Inan, Elizabeth Nielsen, Shahab Raji, Mark Steedman, Matthew Stone
Typologically diverse languages offer systems of lexical and grammatical aspect that allow speakers to focus on facets of event structure in ways that comport with the specific communicative setting and discourse constraints they face.
4 code implementations • Findings (ACL) 2022 • Katherine Atwell, Anthony Sicilia, Seong Jae Hwang, Malihe Alikhani
Our results not only motivate our proposal and help us to understand its limitations, but also provide insight on the properties of discourse models and datasets which improve performance in domain adaptation.
1 code implementation • Findings (ACL) 2022 • Mert İnan, Yang Zhong, Sabit Hassan, Lorna Quandt, Malihe Alikhani
To employ our strategies, we first annotate a subset of the benchmark PHOENIX-14T, a German Sign Language dataset, with different levels of intensification.
no code implementations • 11 Feb 2022 • Carla Viegas, Mert İnan, Lorna Quandt, Malihe Alikhani
State-of-the-art sign language generation frameworks lack expressivity and naturalness which is the result of only focusing manual signs, neglecting the affective, grammatical and semantic functions of facial expressions.
no code implementations • 16 Nov 2021 • Amanda Buddemeyer, Erin Walker, Malihe Alikhani
Many educational technologies use artificial intelligence (AI) that presents generated or produced language to the learner.
1 code implementation • 22 Sep 2021 • Malihe Alikhani, Fangda Han, Hareesh Ravi, Mubbasir Kapadia, Vladimir Pavlovic, Matthew Stone
Common image-text joint understanding techniques presume that images and the associated text can universally be characterized by a single implicit model.
2 code implementations • Findings (EMNLP) 2021 • Mert İnan, Piyush Sharma, Baber Khalid, Radu Soricut, Matthew Stone, Malihe Alikhani
Developers of text generation models rely on automated evaluation metrics as a stand-in for slow and expensive manual evaluations.
1 code implementation • AKBC Workshop CSKB 2021 • Steven Y. Feng, Kevin Lu, Zhuofu Tao, Malihe Alikhani, Teruko Mitamura, Eduard Hovy, Varun Gangal
We investigate the use of multimodal information contained in images as an effective method for enhancing the commonsense of Transformer models for text generation.
1 code implementation • INLG (ACL) 2021 • Chloe Ciora, Nur Iren, Malihe Alikhani
As Machine Translation (MT) has become increasingly more powerful, accessible, and widespread, the potential for the perpetuation of bias has grown alongside its advances.
1 code implementation • 28 Jun 2021 • Barea Sinno, Bernardo Oviedo, Katherine Atwell, Malihe Alikhani, Junyi Jessy Li
Analyzing ideology and polarization is of critical importance in advancing our grasp of modern politics.
1 code implementation • AKBC 2021 • Meiqi Guo, Mingda Zhang, Siva Reddy, Malihe Alikhani
We introduce Abg-CoQA, a novel dataset for clarifying ambiguity in Conversational Question Answering systems.
no code implementations • ACL 2021 • Kayo Yin, Amit Moryossef, Julie Hochgesang, Yoav Goldberg, Malihe Alikhani
Signed languages are the primary means of communication for many deaf and hard of hearing individuals.
1 code implementation • 14 Apr 2021 • Varun Gangal, Steven Y. Feng, Malihe Alikhani, Teruko Mitamura, Eduard Hovy
In this paper, we propose and investigate the task of Narrative Reordering (NAREOR) which involves rewriting a given story in a different narrative order while preserving its plot.
1 code implementation • 11 Dec 2020 • 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 Mahabadi, 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
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 languages like English.
no code implementations • COLING 2020 • Baber Khalid, Malihe Alikhani, Matthew Stone
In many domains, dialogue systems need to work collaboratively with users to successfully reconstruct the meaning the user had in mind.
no code implementations • COLING 2020 • Thomas Kober, Malihe Alikhani, Matthew Stone, Mark Steedman
The interpretation of the lexical aspect of verbs in English plays a crucial role for recognizing textual entailment and learning discourse-level inferences.
no code implementations • 8 Jul 2020 • Baber Khalid, Malihe Alikhani, Michael Fellner, Brian McMahan, Matthew Stone
Prior approaches to realizing mixed-initiative human--computer referential communication have adopted information-state or collaborative problem-solving approaches.
no code implementations • ACL 2020 • Malihe Alikhani, Piyush Sharma, Shengjie Li, Radu Soricut, Matthew Stone
We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning.
no code implementations • ACL 2020 • Malihe Alikhani, Matthew Stone
All communication aims at achieving common ground (grounding): interlocutors can work together effectively only with mutual beliefs about what the state of the world is, about what their goals are, and about how they plan to make their goals a reality.
no code implementations • 2 May 2020 • Malihe Alikhani, Piyush Sharma, Shengjie Li, Radu Soricut, Matthew Stone
We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning.
1 code implementation • 13 Dec 2019 • Malihe Alikhani, Baber Khalid, Rahul Shome, Chaitanya Mitash, Kostas Bekris, Matthew Stone
This work proposes a set of interpretive principles for how a robotic arm can use pointing actions to communicate task information to people by extending existing models from the related literature.
no code implementations • 9 Dec 2019 • Tuomo Hiippala, Malihe Alikhani, Jonas Haverinen, Timo Kalliokoski, Evanfiya Logacheva, Serafina Orekhova, Aino Tuomainen, Matthew Stone, John A. Bateman
This article introduces AI2D-RST, a multimodal corpus of 1000 English-language diagrams that represent topics in primary school natural sciences, such as food webs, life cycles, moon phases and human physiology.
no code implementations • WS 2019 • Malihe Alikhani, Matthew Stone
We study verbs in image{--}text corpora, contrasting \textit{caption} corpora, where texts are explicitly written to characterize image content, with \textit{depiction} corpora, where texts and images may stand in more general relations.
1 code implementation • NAACL 2019 • Malihe Alikhani, Sreyasi Nag Chowdhury, Gerard de Melo, Matthew Stone
This paper presents a novel crowd-sourced resource for multimodal discourse: our resource characterizes inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations.
1 code implementation • COLING 2018 • Malihe Alikhani, Matthew Stone
Arrows are a key ingredient of schematic pictorial communication.