Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation (2020.emnlp-main)
Copied to clipboard
| Challenge: | a new toolkit for localizing a semantic parser for a language is proposed . the proposed approach is based on a method for question answering systems . |
| Approach: | They propose a toolkit that leverages Neural Machine Translation systems to localize a semantic parser for a new language. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in 10 new languages . it can be deployed in restaurants and hotels in less than 24 hours . |
Similar Papers
Bootstrapping a Crosslingual Semantic Parser (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in semantic parsing are limited to English but professional translation can be prohibitively expensive. |
| Approach: | They adapt a semantic parser trained on a single language to new languages and multiple domains with minimal annotation. |
| Outcome: | The proposed approach achieves parsing accuracy within 2% of translation using only 50% of training data. |
Analyzing Challenges in Neural Machine Translation for Software Localization (2023.eacl-main)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) is a new form of machine translation that reduces the post-editing time of human annotators. |
| Approach: | They propose to use a novel multilingual UI corpus collection to test NMT for user interfaces. |
| Outcome: | The proposed test set evaluates state-of-the-art methods on a UI translation task from English to German and identifies its limitations. |
Neural Semantic Parsing (P18-5)
Copied to clipboard
| Challenge: | Semantic parsing is the study of translating natural language utterances into machine-executable programs. |
| Approach: | They will describe the various approaches researchers have taken to translate natural language into a formal language . they will also discuss why much recent work has chosen to use standard programming languages instead of more linguistically-motivated representations. |
| Outcome: | This paper will describe the various approaches researchers have taken to translate natural language into a formal language. |
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards (2023.findings-emnlp)
Copied to clipboard
Baban Gain, Ramakrishna Appicharla, Soumya Chennabasavaraj, Nikesh Garera, Asif Ekbal, Muthusamy Chelliah
| Challenge: | Creating a synthetic parallel corpus from noisy data is also difficult due to its noisy nature. |
| Approach: | They propose a training methodology that fine-tunes the NMT system only using source-side data to balance adequacy and fluency. |
| Outcome: | The proposed method surpasses the MLE-based fine-tuning approach by achieving a 1.9 BLEU improvement. |
Using Semantic Role Labeling to Improve Neural Machine Translation (2022.lrec-1)
Copied to clipboard
| Challenge: | despite progress in machine translation, some form of language understanding may be desirable . current systems rely on pattern recognition, but some form may be useful . |
| Approach: | They use semantic role labeling to annotate a standard parallel corpus with semantic roles . they then train a neural machine translation system using the annotated corpus and original unannotated text . |
| Outcome: | The proposed system improves BLEU scores for English, French, German, Greek and Spanish. |
LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systems (2024.acl-demos)
Copied to clipboard
| Challenge: | Existing tools for augmented question-answering do not support researchers and developers to customize the training, testing, and deployment process. |
| Approach: | They propose an open-source toolkit that features a wide selection of model training algorithms, evaluation methods, and deployment tools curated from the latest research. |
| Outcome: | The proposed framework trains and deploys 7B-models with the same performance as OpenAI’s text-ada-002 and GPT-4-turbo. |
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to generate semantic parsers that answer questions on databases require large amounts of annotated data. |
| Approach: | They propose a method to generate semantic parsers that answer questions on databases . they use automatic paraphrasing and template-based parsing to find alternative expressions . |
| Outcome: | The proposed method achieves 69.8% answer accuracy on natural questions, 16.4% higher than state-of-the-art models and 5.2% lower than the same model trained with human data. |
QASem Parsing: Text-to-text Modeling of QA-based Semantics (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work suggests the appeals of incorporating explicit semantic representations into NLP . semi-structured natural language structures provide an intermediate meaning-capturing representation . |
| Approach: | They propose a semi-structured natural-language representation of textual information . they examine input and output linearization strategies and multitask learning . |
| Outcome: | The proposed model is based on pre-trained sequence-to-sequence language models . it is easy to use and can be used for downstream tasks that benefit from it . |
Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing approaches to understanding textual information are still far from achieving true natural language understanding (NLU). |
| Approach: | They propose a unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantics Parsing. |
| Outcome: | The proposed system can provide users with rich and diverse semantic information, help second-language learners, and integrate explicit semantic knowledge into downstream tasks and real-world applications. |
Effective QA-Driven Annotation of Predicate–Argument Relations Across Languages (2026.eacl-long)
Copied to clipboard
| Challenge: | Explicit representations of predicate-argument relations are a cornerstone of natural language understanding. |
| Approach: | They propose a cross-linguistic projection approach that reuses an English QA-SRL parser within a constrained translation and word-alignment pipeline to automatically generate question-answer annotations aligned with target-language predicates. |
| Outcome: | The proposed approach outperforms strong multilingual LLMs in Hebrew, Russian, and French. |