Papers with in-house
Computer Assisted Annotation of Tension Development in TED Talks through Crowdsourcing (D19-59)
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| Challenge: | Using a neural network, we annotate whether tension is increasing, decreasing, or staying unchanged. |
| Approach: | They propose a machine-assisted method for the identification of tension development using a neural network based prediction model. |
| Outcome: | The proposed method is compared with other methods in in-house and crowdsourced environments. |
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding (2022.findings-aacl)
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| Challenge: | Meta-Learning requires a large number of training tasks to learn representations that transfer well to unseen tasks. |
| Approach: | They propose a method which synthesizes new tasks by linearly interpolating existing tasks. |
| Outcome: | The proposed method outperforms baselines and does not degrade performance even when it is high. |
Improve Speech Translation Through Text Rewrite (2025.coling-industry)
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| Challenge: | Recent advances in speech translation (ST) research have focused on the unique characteristics of spontaneous speech, including accents and presentation quality. |
| Approach: | They propose to transform transcribed speech into a cleaner style more in line with the expectations of translation models built from written text. |
| Outcome: | Experiments on public and in-house translation models show that the proposed model can be effectively distilled into a standalone translation model. |
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping (D19-1)
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| Challenge: | Relation extraction (RE) is an important information extraction task that seeks to detect and classify semantic relationships between entities. |
| Approach: | They propose a bilingual word embedding mapping approach for cross-lingual RE model transfer . they use a small bilingual dictionary with only 1K word pairs to embed word pairs . |
| Outcome: | The proposed approach achieves very good performance on target and target languages . it uses bilingual word embedding mapping to transfer a source-language model . |
Multimodal Context Carryover (2022.emnlp-industry)
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Prashan Wanigasekara, Nalin Gupta, Fan Yang, Emre Barut, Zeynab Raeesy, Kechen Qin, Stephen Rawls, Xinyue Liu, Chengwei Su, Spurthi Sandiri
| Challenge: | Existing voice-only dialogue systems lack multimodality support, which can lead to costly system redesigns. |
| Approach: | They propose to augment existing voice-only dialogue systems with additional multimodal components to facilitate quick delivery of visual modality support with minimal changes. |
| Outcome: | The proposed framework improves visual modality support with minimal changes on an in-house multi-modal visual navigation data set. |
Probing the Depths of Language Models’ Contact-Center Knowledge for Quality Assurance (2024.emnlp-industry)
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| Challenge: | Recent advances in large Language Models (LMs) have significantly enhanced their capabilities across various domains, including natural language understanding and domain knowledge. |
| Approach: | They propose methods to transfer domain-specific knowledge to smaller models by leveraging evaluation plans generated by more knowledgeable models with optional human-in-the-loop refinement to enhance the capabilities of smaller models. |
| Outcome: | The proposed models improve 18.95% on an in-house QA dataset on a contact-center quality assurance task. |
Task-Driven and Experience-Based Question Answering Corpus for In-Home Robot Application in the House3D Virtual Environment (2022.lrec-1)
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| Challenge: | Question answering is an important part of natural language processing (NLP) |
| Approach: | They propose to use TEQA to investigate the ability of agent task experience understanding for the long-term household task. |
| Outcome: | The proposed corpus aims to investigate the ability of task experience understanding of agents for the daily question answering scenario on the ALFRED dataset. |
RSC: A Romanian Read Speech Corpus for Automatic Speech Recognition (2020.lrec-1)
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| Challenge: | Romanian language is under-resourced due to the lack of acoustic and linguistic resources. |
| Approach: | They propose to use a Romanian speech corpus to train automatic speech recognition algorithms based on the spoken hotword detection mechanism. |
| Outcome: | The read speech corpus is a speech recognition system that can perform automatic speech recognition and speech synthesis using state-of-the-art speech recognition toolkit. |
KazQAD: Kazakh Open-Domain Question Answering Dataset (2024.lrec-main)
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| Challenge: | KazQAD contains just under 6,000 unique questions with extracted short answers and nearly 12,000 passage-level relevance judgements. |
| Approach: | They introduce a Kazakh open-domain question answering dataset that can be used in reading comprehension and full ODQA settings. |
| Outcome: | The proposed dataset can be used in reading comprehension and full ODQA settings, as well as for information retrieval experiments. |