Papers with News
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering (2023.tacl-1)
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Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, Suranga Nanayakkara
| Challenge: | Retrieval Augment Generation (RAG) has been developed for use in open-domain question answering (ODQA) but it is not optimized for use with other specialized domains such as healthcare and news. |
| Approach: | They propose an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. |
| Outcome: | The proposed extension can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. |
Simple Neologism Based Domain Independent Models to Predict Year of Authorship (C18-1)
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| Challenge: | Using domain independent models, we date documents based only on neologism usage patterns . nasa models use only 200 input features, compared to state of the art models using 200K features. |
| Approach: | They propose domain independent models to date documents based only on neologism usage patterns. |
| Outcome: | The proposed models can generalize to various domains like News, Fiction, and Non-Fiction with competitive performance. |
Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation (2024.findings-eacl)
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| Challenge: | Current multilingual agreement (MA) methods require parallel data between multiple language pairs, which is not always realistic and optimize the agreement in an ambiguous direction, which hampers the translation performance. |
| Approach: | They propose a novel multilingual agreement framework that optimizes agreement bidirectionally with the Kullback-Leibler Divergence loss. |
| Outcome: | The proposed method improves strong baselines on the task of multilingual neural machine translation with three benchmarks: TED Talks, News, and Europarl. |
Improving Topic Quality by Promoting Named Entities in Topic Modeling (P18-2)
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| Challenge: | Using named entities as domain-specific terms for news-centric content has not been studied extensively. |
| Approach: | They propose to use named entities as domain-specific terms for news-centric content . they propose a weighting model that incorporates more named entities in topic descriptors . |
| Outcome: | The proposed model improves the quality of news-centric topics by including more named entities in the topic descriptors. |
Exploring Paracrawl for Document-level Neural Machine Translation (2023.eacl-main)
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| Challenge: | Document-level neural machine translation (NMT) has outperformed sentence-level NMT on a number of datasets. |
| Approach: | They use Paracrawl to extract parallel paragraphs from Paracral webpages . they also use the extracted parallel paragraph as parallel documents for training . |
| Outcome: | The proposed model outperforms sentence-level NMT on a number of datasets. |
Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation (2022.findings-naacl)
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| Challenge: | Recent studies have shown that the effective use of contextual information between sentences can achieve better performance in document-level machine translation. |
| Approach: | They propose a recurrent memory unit to the Transformer to support the information exchange between the sentence and previous context. |
| Outcome: | The proposed model outperforms the previous work on TED and News by 0.91 s-BLEU and 1.49 d-BLUE on average. |
TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection (2021.emnlp-main)
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| Challenge: | Existing approaches to solve domain shifts in NLP tasks require additional pre-training . current approaches focus on the downstream corpus when it is small, but are not effective . |
| Approach: | They propose a task-adapted pre-training framework that can be used when the downstream corpus is too small for additional pre-tuning. |
| Outcome: | The proposed framework outperforms baseline methods on biomedical, computer science, news, and movie reviews tasks. |
My side, your side and the evidence: Discovering aligned actor groups and the narratives they weave (2023.acl-long)
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| Challenge: | Identify distinct sets of aligned story actors responsible for sustaining issue-specific narratives . authors propose a novel two-step graph-based framework that identifies alignments between actors . |
| Approach: | They propose a proxy task to identify the distinct sets of aligned story actors . they propose identifying alignments between actors and extracting alignes using TAMPA . |
| Outcome: | The proposed framework is based on a corpus of text segments associated with six issues . it identifies aligned actors and extracts alignable actor groups from the network structure . |
Target-Side Augmentation for Document-Level Machine Translation (2023.acl-long)
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| Challenge: | Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data. |
| Approach: | They propose a document-level machine translation model that generates many potential translations for each source document and smoothes the distribution. |
| Outcome: | The proposed method outperforms the previous best system by 2.30 s-BLEU on News and achieves new state-of-the-art on News . |
POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training (2020.emnlp-main)
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| Challenge: | Existing pre-trained language models cannot be directly employed to generate text under specified lexical constraints. |
| Approach: | They propose a method for insertion-based text generation that inserts tokens between existing tokens in a parallel manner. |
| Outcome: | The proposed method is intuitive and interpretable on Wikipedia and Yelp datasets. |
Multi-Label and Multilingual News Framing Analysis (2020.acl-main)
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| Challenge: | Recent studies have focused on news framing in English, but few studies have explored how it can be extended to other languages and in multi-label settings. |
| Approach: | They propose a method that leverages dictionary and few annotations to detect frames from just the headline in a low-resource context. |
| Outcome: | The proposed method performs better than translating the entire headline to the source language . it can be scaled up to many languages, even those without existing translation technologies . |