Search Results for author: Malihe Alikhani

Found 27 papers, 14 papers with code

Where Are We in Discourse Relation Recognition?

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.

Discourse Parsing

Signed Coreference Resolution

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.

Coreference Resolution

The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error

1 code implementation 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.

Discourse Parsing Domain Adaptation

Modeling Intensification for Sign Language Generation: A Computational Approach

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.

Text Generation

Including Facial Expressions in Contextual Embeddings for Sign Language Generation

no code implementations11 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.

Text Generation

Words of Wisdom: Representational Harms in Learning From AI Communication

no code implementations16 Nov 2021 Amanda Buddemeyer, Erin Walker, Malihe Alikhani

Many educational technologies use artificial intelligence (AI) that presents generated or produced language to the learner.

Question Generation

Cross-Modal Coherence for Text-to-Image Retrieval

1 code implementation22 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.

Image Retrieval Text-to-Image Retrieval

Examining Covert Gender Bias: A Case Study in Turkish and English Machine Translation Models

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.

Machine Translation Translation

Including Signed Languages in Natural Language Processing

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.

NAREOR: The Narrative Reordering Problem

1 code implementation14 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.

Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue

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.

reinforcement-learning

Aspectuality Across Genre: A Distributional Semantics Approach

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.

Natural Language Inference

Discourse Coherence, Reference Grounding and Goal Oriented Dialogue

no code implementations8 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.

reinforcement-learning

Achieving Common Ground in Multi-modal Dialogue

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.

Cross-modal Coherence Modeling for Caption Generation

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.

Image Captioning

Clue: Cross-modal Coherence Modeling for Caption Generation

no code implementations2 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.

Image Captioning

That and There: Judging the Intent of Pointing Actions with Robotic Arms

1 code implementation13 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.

Common Sense Reasoning

AI2D-RST: A multimodal corpus of 1000 primary school science diagrams

no code implementations9 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.

Question Answering Visual Question Answering

``Caption'' as a Coherence Relation: Evidence and Implications

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.

Image Retrieval

CITE: A Corpus of Image-Text Discourse 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.

Common Sense Reasoning

Arrows are the Verbs of Diagrams

1 code implementation COLING 2018 Malihe Alikhani, Matthew Stone

Arrows are a key ingredient of schematic pictorial communication.

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