Papers by Reno Kriz
Learning Translations via Images with a Massively Multilingual Image Dataset (P18-1)
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| Challenge: | Existing datasets for learning translations of words are limited to a few high-resource languages and unrealistically easy settings. |
| Approach: | They propose a large-scale multilingual corpus of images labeled with the word they represent to facilitate translation research. |
| Outcome: | The proposed method improves on an unsupervised technique that has been limited to a few languages and unrealistic settings. |
Simplification Using Paraphrases and Context-Based Lexical Substitution (N18-1)
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| Challenge: | Lexical simplification involves identifying complex words or phrases that need to be simplified and suggesting simpler meaning-preserving substitutes. |
| Approach: | They propose a complex word identification model that exploits both lexical and contextual features and a word-embedding lexical substitution model to replace the detected complex words with simpler paraphrases. |
| Outcome: | The proposed model detects complex words with higher accuracy than other models and proposes good substitutes in context. |
Grounding Partially-Defined Events in Multimodal Data (2024.findings-emnlp)
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Kate Sanders, Reno Kriz, David Etter, Hannah Recknor, Alexander Martin, Cameron Carpenter, Jingyang Lin, Benjamin Van Durme
| Challenge: | Evidence suggests prelinguistic infants are capable of recognizing discrete events in real-world stimuli. |
| Approach: | They propose a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task. |
| Outcome: | The proposed approach can extract events from 14.5 hours of annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities. |
BiSECT: Learning to Split and Rephrase Sentences with Bitexts (2021.emnlp-main)
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| Challenge: | Several past efforts have created Split and Rephrase training sets, which consist of long, complex input sentences paired with multiple shorter sentences that preserve the meaning of the input sentence. |
| Approach: | They propose a new dataset and a model for this task by extracting 1-2 sentence alignments from bilingual parallel corpora and using machine translation to convert both sides of the corpus into the same language. |
| Outcome: | The proposed model can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations. |
Did that happen? Predicting Social Media Posts that are Indicative of what happened in a scene: A case study of a TV show (2022.lrec-1)
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| Challenge: | Prior work identified and summarized scenes associated with a TV show by selecting a few representative social media posts (5 posts) that were published during the timeline of the scenes. |
| Approach: | They propose a method to predict social media posts associated with a TV show from those that are not-indicative. |
| Outcome: | The proposed method can predict posts indicative of what happened in a scene from those that are not-indicative based on high AUC's on social media posts associated with a popular TV show . |
Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification (N19-1)
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Reno Kriz, João Sedoc, Marianna Apidianaki, Carolina Zheng, Gaurav Kumar, Eleni Miltsakaki, Chris Callison-Burch
| Challenge: | Recent research has applied sequence-to-sequence (Seq2Sequen) models to text simplification . generic models tend to copy directly from the original sentence, resulting in outputs that are long and complex. |
| Approach: | They propose to incorporate word complexities into the loss function during training and generate a large set of diverse candidate simplifications at test time. |
| Outcome: | The proposed model can perform competitively with state-of-the-art systems while generating simpler sentences. |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
Comparison of Diverse Decoding Methods from Conditional Language Models (P19-1)
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| Challenge: | Conditional language models can generate a diverse set of outputs, but for open-ended tasks, beam search is ill-suited to generating a set of diverse sequences. |
| Approach: | They propose a method where we over-sample candidates and use clustering to remove similar sequences to achieve high diversity without sacrificing quality. |
| Outcome: | The proposed method over-samples candidates and removes similar sequences to achieve high diversity without sacrificing quality. |
On Event Individuation for Document-Level Information Extraction (2023.findings-emnlp)
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| Challenge: | a bomb exploded in a restaurant in Lima, and a second device was deactivated by the police . |
| Approach: | They argue that the task demands definitive answers to thorny questions of *event individuation* they argue that even human experts disagree on the task . |
| Outcome: | The proposed task demands definitive answers to thorny questions of *event individuation* . the proposed task also raises concerns about the usefulness of template filling metrics . |
WikiVideo: Article Generation from Multiple Videos (2026.findings-acl)
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Alexander Martin, Reno Kriz, William Gantt Walden, Kate Sanders, Hannah Recknor, Eugene Yang, Francis Ferraro, Benjamin Van Durme
| Challenge: | Existing methods for retrieval-augmented generation focus on text rather than video. |
| Approach: | They propose a benchmark to generate Wikipedia-style articles from multiple videos . they propose 'collaborative article generation' that leverages an r1-style reasoning model and a VideoLLM to draw higher-level inferences about the target event than is possible with VideoLLms alone. |
| Outcome: | The proposed method outperforms existing methods in oracle retrieval and RAG settings while suggesting promising avenues for future work. |
Whisper-UT: A Unified Translation Framework for Speech and Text (2025.emnlp-main)
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Cihan Xiao, Matthew Wiesner, Debashish Chakraborty, Reno Kriz, Keith Cunningham, Kenton Murray, Kevin Duh, Luis Tavarez-Arce, Paul McNamee, Sanjeev Khudanpur
| Challenge: | Encoder-decoder models have achieved remarkable success in speech and text tasks, but efficiently adapting them to diverse uni/multimodal scenarios remains a challenge. |
| Approach: | They propose a framework that leverages lightweight adapters to enable seamless adaptation across tasks. |
| Outcome: | The proposed framework improves speech translation performance through a 2-stage decoding strategy without requiring 3-way parallel data. |