Papers with neural systems
Multilingual Whispers: Generating Paraphrases with Translation (D19-55)
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| Challenge: | Humans naturally paraphrase, but they can generate approximately the same meaning with a different surface realization. |
| Approach: | They compare translation-based paraphrase gathering using human, automatic, or hybrid techniques to monolingual paraphrasing by experts and non-experts. |
| Outcome: | The proposed methods outperform human translation systems in a variety of translation tasks. |
Learning with Limited Text Data (2022.acl-tutorials)
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| Challenge: | Natural Language Processing (NLP) relies on labeled data to perform state-of-the-art performance . labeles are often required to label large amounts of textual data . this tutorial will provide an overview of labeleing in NLP . |
| Approach: | This tutorial will provide a systematic overview of methods for learning from limited labeled data. |
| Outcome: | This tutorial will provide a systematic and up-to-date overview of the proposed methods . it will highlight current challenges and future directions . |
A Tour of Explicit Multilingual Semantics: Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing (2022.aacl-tutorials)
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| Challenge: | a recent advent of pretrained language models has sparked a revolution in NLP . but, there are still questions about whether current approaches capture explicit, symbolic meaning . this tutorial will review efforts to tackle three key open problems in lexical and sentence-level semantics . |
| Approach: | This tutorial reviews recent efforts to shed light on meaning in NLP . it will focus on three key open problems in lexical and sentence-level semantics . |
| Outcome: | This tutorial reviews recent efforts to shed light on meaning in NLP . it focuses on three key open problems in lexical and sentence-level semantics . |
Semantic Expressive Capacity with Bounded Memory (P19-1)
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| Challenge: | Existing methods for semantic parsing use placeholders to represent relations between sentences and semantic representations. |
| Approach: | They show that compositional parsers can remember unbounded number of placeholders . this is the first study of this kind to describe relations between sentences and semantic representations based on projective mechanisms. |
| Outcome: | The proposed method can represent relations between sentences and semantic representations without using nonprojective mechanisms. |
Sketch-Driven Regular Expression Generation from Natural Language and Examples (2020.tacl-1)
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| Challenge: | Recent systems for converting natural language descriptions into regexes have achieved some success, but typically deal with short, formulaic text and can only produce simple regexe. |
| Approach: | They propose a framework for regex synthesis in a context where both natural language and examples are available. |
| Outcome: | The proposed framework achieves state-of-the-art on two prior datasets and a real-world dataset, which existing neural systems completely fail on. |
Okay, Let’s Do This! Modeling Event Coreference with Generated Rationales and Knowledge Distillation (2024.naacl-long)
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| Challenge: | Recent work shows that generative large language models (LLMs) can be used to solve cross-document coreference problems. |
| Approach: | They propose rationale-oriented event clustering and knowledge distillation methods for event coreference scoring that leverage enriched information from the FTRs for improved CDCR. |
| Outcome: | The proposed model achieves SOTA B3 F1 on the ECB+ and GVC corpora without additional annotation or expensive document clustering. |
Benchmarking Neural and Statistical Machine Translation on Low-Resource African Languages (2020.lrec-1)
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| Challenge: | a recent study has focused on languages where large amounts of resources are available. |
| Approach: | They benchmark state of the art statistical and neural machine translation systems on Somali and Swahili languages . they find that statistical machine translation and neural translation can perform similarly in low-resource scenarios . |
| Outcome: | The results show that statistical machine translation and neural machine translation perform similarly in low-resource scenarios. |
SimQA: Detecting Simultaneous MT Errors through Word-by-Word Question Answering (2022.emnlp-main)
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| Challenge: | a good SimulMT system will allow the downstream QA system to answer correctly as quickly as possible. |
| Approach: | They propose a word-by-word question answering evaluation task to evaluate if models translate salient elements of a question correctly. |
| Outcome: | a new evaluation task aims to show whether models translate salient elements of a question accurately and quickly . evaluators can reveal weaknesses in existing neural systems, hallucinating or omitting facts . human evaluation is too costly and slow to guide system development, authors say . |
A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality? (2020.lrec-1)
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Julia Ive, Lucia Specia, Sara Szoc, Tom Vanallemeersch, Joachim Van den Bogaert, Eduardo Farah, Christine Maroti, Artur Ventura, Maxim Khalilov
| Challenge: | Current state-of-the-art in Neural Machine Translation (NMT) has reached remarkable progress, but human evaluations are often judged as having lower quality than top NMT systems. |
| Approach: | They propose to use a machine translation dataset with post-edited high-quality neural machine translation and independent human references to compare the results. |
| Outcome: | The proposed dataset includes 31K tuples including a source sentence, the respective machine translation by a neural machine translation system, and a post-edited version of such translation by professional translator. |
Lexicosyntactic Inference in Neural Models (D18-1)
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| Challenge: | lexicosyntactic inferences are triggered by surprising aspects of the syntactical context that a word occurs in. |
| Approach: | They build a factuality judgment dataset for English clause-embedding verbs in various syntactic contexts and use it to probe the behavior of current state-of-the-art neural systems. |
| Outcome: | The proposed model makes systematic errors that are visible through the lens of factuality prediction. |
Decoding a Neural Retriever’s Latent Space for Query Suggestion (2022.emnlp-main)
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Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann
| Challenge: | Neural retrieval models have replaced bag-of-words methods for document retrieval . however, they lack the interpretability of bag-off-word models . |
| Approach: | They train a query decoder that generates a meaningful query from a latent representation of a neural search engine. |
| Outcome: | The proposed model outperforms both query reformulation and PRF information retrieval baselines. |
Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses (D19-1)
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| Challenge: | Sentence position is a strong feature for news summarization, since the lead often summarizes the key points of the article. |
| Approach: | They propose two techniques to make neural systems sensitive to the importance of content in different parts of the article by using random shuffled sentences to pretrain the model. |
| Outcome: | The proposed techniques improve the performance of a competitive reinforcement learning based extractive system, with the auxiliary loss being more powerful than pretraining. |
ToolWriter: Question Specific Tool Synthesis for Tabular Data (2023.emnlp-main)
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| Challenge: | Tabular question answering (TQA) requires joint reasoning of natural language with large amounts of semi-structured data. |
| Approach: | They propose to use query-specific programs to generate query-based tools to simplify large tables and detect when to apply them to transform tables. |
| Outcome: | The proposed tool improves state-of-the-art on two tabular question-answering datasets. |