Mert Inan


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Including Facial Expressions in Contextual Embeddings for Sign Language Generation
Carla Viegas | Mert Inan | Lorna Quandt | Malihe Alikhani
Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)

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. The purpose of this work is to augment semantic representation of sign language through grounding facial expressions. We study the effect of modeling the relationship between text, gloss, and facial expressions on the performance of the sign generation systems. In particular, we propose a Dual Encoder Transformer able to generate manual signs as well as facial expressions by capturing the similarities and differences found in text and sign gloss annotation. We take into consideration the role of facial muscle activity to express intensities of manual signs by being the first to employ facial action units in sign language generation. We perform a series of experiments showing that our proposed model improves the quality of automatically generated sign language.

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Multimodal Embodied Plan Prediction Augmented with Synthetic Embodied Dialogue
Aishwarya Padmakumar | Mert Inan | Spandana Gella | Patrick Lange | Dilek Hakkani-Tur
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Embodied task completion is a challenge where an agent in a simulated environment must predict environment actions to complete tasks based on natural language instructions and ego-centric visual observations. We propose a variant of this problem where the agent predicts actions at a higher level of abstraction called a plan, which helps make agent actions more interpretable and can be obtained from the appropriate prompting of large language models. We show that multimodal transformer models can outperform language-only models for this problem but fall significantly short of oracle plans. Since collecting human-human dialogues for embodied environments is expensive and time-consuming, we propose a method to synthetically generate such dialogues, which we then use as training data for plan prediction. We demonstrate that multimodal transformer models can attain strong zero-shot performance from our synthetic data, outperforming language-only models trained on human-human data.

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Learning Multimodal Cues of Children’s Uncertainty
Qi Cheng | Mert Inan | Rahma Mbarki | Grace Grmek | Theresa Choi | Yiming Sun | Kimele Persaud | Jenny Wang | Malihe Alikhani
Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Understanding uncertainty plays a critical role in achieving common ground (Clark et al., 1983). This is especially important for multimodal AI systems that collaborate with users to solve a problem or guide the user through a challenging concept. In this work, for the first time, we present a dataset annotated in collaboration with developmental and cognitive psychologists for the purpose of studying nonverbal cues of uncertainty. We then present an analysis of the data, studying different roles of uncertainty and its relationship with task difficulty and performance. Lastly, we present a multimodal machine learning model that can predict uncertainty given a real-time video clip of a participant, which we find improves upon a baseline multimodal transformer model. This work informs research on cognitive coordination between human-human and human-AI and has broad implications for gesture understanding and generation. The anonymized version of our data and code will be publicly available upon the completion of the required consent forms and data sheets.

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Findings of the Second WMT Shared Task on Sign Language Translation (WMT-SLT23)
Mathias Müller | Malihe Alikhani | Eleftherios Avramidis | Richard Bowden | Annelies Braffort | Necati Cihan Camgöz | Sarah Ebling | Cristina España-Bonet | Anne Göhring | Roman Grundkiewicz | Mert Inan | Zifan Jiang | Oscar Koller | Amit Moryossef | Annette Rios | Dimitar Shterionov | Sandra Sidler-Miserez | Katja Tissi | Davy Van Landuyt
Proceedings of the Eighth Conference on Machine Translation

This paper presents the results of the Second WMT Shared Task on Sign Language Translation (WMT-SLT23; This shared task is concerned with automatic translation between signed and spoken languages. The task is unusual in the sense that it requires processing visual information (such as video frames or human pose estimation) beyond the well-known paradigm of text-to-text machine translation (MT). The task offers four tracks involving the following languages: Swiss German Sign Language (DSGS), French Sign Language of Switzerland (LSF-CH), Italian Sign Language of Switzerland (LIS-CH), German, French and Italian. Four teams (including one working on a baseline submission) participated in this second edition of the task, all submitting to the DSGS-to-German track. Besides a system ranking and system papers describing state-of-the-art techniques, this shared task makes the following scientific contributions: novel corpora and reproducible baseline systems. Finally, the task also resulted in publicly available sets of system outputs and more human evaluation scores for sign language translation.

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Proceedings of the 3rd Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics (SpLU-RoboNLP 2023)
Aishwarya Padmakumar | Mert Inan | Yue Fan | Xin Wang | Malihe Alikhani
Proceedings of the 3rd Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics (SpLU-RoboNLP 2023)


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Zero-shot Cross-Linguistic Learning of Event Semantics
Malihe Alikhani | Thomas Kober | Bashar Alhafni | Yue Chen | Mert Inan | Elizabeth Nielsen | Shahab Raji | Mark Steedman | Matthew Stone
Proceedings of the 15th International Conference on Natural Language Generation

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Modeling Intensification for Sign Language Generation: A Computational Approach
Mert Inan | Yang Zhong | Sabit Hassan | Lorna Quandt | Malihe Alikhani
Findings of the Association for Computational Linguistics: ACL 2022

End-to-end sign language generation models do not accurately represent the prosody in sign language. A lack of temporal and spatial variations leads to poor-quality generated presentations that confuse human interpreters. In this paper, we aim to improve the prosody in generated sign languages by modeling intensification in a data-driven manner. We present different strategies grounded in linguistics of sign language that inform how intensity modifiers can be represented in gloss annotations. To employ our strategies, we first annotate a subset of the benchmark PHOENIX-14T, a German Sign Language dataset, with different levels of intensification. We then use a supervised intensity tagger to extend the annotated dataset and obtain labels for the remaining portion of it. This enhanced dataset is then used to train state-of-the-art transformer models for sign language generation. We find that our efforts in intensification modeling yield better results when evaluated with automatic metrics. Human evaluation also indicates a higher preference of the videos generated using our model.


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COSMic: A Coherence-Aware Generation Metric for Image Descriptions
Mert Inan | Piyush Sharma | Baber Khalid | Radu Soricut | Matthew Stone | Malihe Alikhani
Findings of the Association for Computational Linguistics: EMNLP 2021

Developers of text generation models rely on automated evaluation metrics as a stand-in for slow and expensive manual evaluations. However, image captioning metrics have struggled to give accurate learned estimates of the semantic and pragmatic success of output text. We address this weakness by introducing the first discourse-aware learned generation metric for evaluating image descriptions. Our approach is inspired by computational theories of discourse for capturing information goals using coherence. We present a dataset of image–description pairs annotated with coherence relations. We then train a coherence-aware metric on a subset of the Conceptual Captions dataset and measure its effectiveness—its ability to predict human ratings of output captions—on a test set composed of out-of-domain images. We demonstrate a higher Kendall Correlation Coefficient for our proposed metric with the human judgments for the results of a number of state-of-the-art coherence-aware caption generation models when compared to several other metrics including recently proposed learned metrics such as BLEURT and BERTScore.