| Challenge: | a monolingual alignment system is ill-suited for word- or short phrase-based alignments. |
| Approach: | They propose a monolingual alignment system for long, sentence- or clause-level alignments . they show that systems designed for word- or short phrase-based alignment are ill-suited for longer alignments. |
| Outcome: | The proposed system outperforms state-of-the-art systems on long alignments . it achieves significantly higher recall on aligning phrases of four or more words . |
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| Challenge: | Experimental results show that our proposed model outperforms all previous approaches for monolingual word alignment. |
| Approach: | They propose a neural semi-Markov CRF alignment model which unifies word and phrase alignments through variable-length spans. |
| Outcome: | The proposed model outperforms existing models on in-domain and out-of-domain evaluations and a QA-based benchmark with human annotations. |
Simple and Effective Text Matching with Richer Alignment Features (P19-1)
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| Challenge: | Existing models only use a single inter-sequence alignment layer to make full use of this process. |
| Approach: | They propose to keep three key features available for inter-sequence alignment . they conduct experiments on four well-studied benchmark datasets . |
| Outcome: | The proposed model is able to perform on four well-studied datasets with fewer parameters and the inference speed is at least 6 times faster than similar models. |
Neuralign: A Context-Aware, Cross-Lingual and Fully-Neural Sentence Alignment System for Long Texts (2024.eacl-long)
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| Challenge: | Existing sentence alignment systems focus on auxiliary information such as document metadata and hyperparameter-sensitive techniques, and neglect the crucial role that context plays in the alignment process. |
| Approach: | They propose a context-aware, end-to-end and fully-neural architecture for sentence alignment that maps source and target sentences in long documents by contextualizing their sentence embeddings with respect to the other sentences in the document. |
| Outcome: | The proposed system maps source and target sentences in long documents by contextualizing their sentence embeddings with respect to the other sentences in the document. |
SentAlign: Accurate and Scalable Sentence Alignment (2023.emnlp-demo)
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| Challenge: | SentAlign is an automatic sentence alignment tool designed for large documents . it evaluates all possible alignment paths in documents of thousands of sentences . |
| Approach: | They present a sentence alignment tool that evaluates all possible alignment paths in parallel documents of thousands of sentences and uses a divide-and-conquer approach to align documents containing tens of thousands. |
| Outcome: | The proposed tool outperforms five other sentence alignment tools on two evaluation sets and on a downstream machine translation task. |
SpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP (2020.coling-main)
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| Challenge: | Existing methods for automatic sentence alignment assume monotonic alignments, but they can handle non-monotonic alignments. |
| Approach: | They propose a method to automatically extract parallel sentences from noisy parallel documents by embeddings and encoding each source and target sentence. |
| Outcome: | The proposed method improves translation accuracy by 4.1 BLEU scores on English-Japanese . it can predict spans in target document from sentences in source document . |
Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing (2020.coling-main)
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| Challenge: | Existing methods for multilingual semantic parsing only handle monolingual parsers, while in real world applications such as Chatbot and search engine, we generally need to handle multi-lingual semanticparsing. |
| Approach: | They propose a multi-level alignment pretraining method in a unified architecture for multi-lingual semantic parsing. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a publicly avail-able multi-lingual semantic parsing dataset and a newly constructed dataset. |
Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders (2022.coling-1)
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| Challenge: | Existing paraphrase identification datasets exhibit high correlation between positive pairs and the degree of their lexical overlap. |
| Approach: | They propose to combine sentence encoders with an alignment component by representing each sentence as a list of predicate-argument spans and decomposing the sentence-level meaning comparison into the alignment between their spans. |
| Outcome: | The proposed approach improves performance and interpretability for various sentence encoders. |
SAPPHIRE: Simple Aligner for Phrasal Paraphrase with Hierarchical Representation (2020.lrec-1)
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| Challenge: | Monolingual phrase alignment is a fundamental problem in natural language understanding and crucial technique in various applications. |
| Approach: | They propose a simple Aligner for Phrasal Paraphrase with HIerarchical REpresentation that uses word embeddings to train phrase alignments. |
| Outcome: | The proposed algorithm outperforms the previous methods and establishes the state-of-the-art. |
Neural CRF Model for Sentence Alignment in Text Simplification (2020.acl-main)
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| Challenge: | Text simplification systems are based on the quality and quantity of complex-simple sentence pairs extracted by aligning sentences between parallel articles. |
| Approach: | They propose a neural CRF alignment model which leverages the sequential nature of sentences in parallel documents and utilizes a sentence pair model to capture semantic similarity. |
| Outcome: | The proposed model outperforms previous work on monolingual sentence alignment task by more than 5 points in F1. |
Compositional Phrase Alignment and Beyond (2020.emnlp-main)
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| Challenge: | Phrase alignment is the basis for sentence pair interactions, such as paraphrase identification and textual entailment recognition. |
| Approach: | They propose a phrase alignment model that embeds similarity distributions into powerful contextualized representations that can be used to model sentence pair interactions. |
| Outcome: | The proposed method significantly outperforms that used in a previous study and achieves a performance competitive with that of experienced human annotators. |