Papers with aligner
Neural Transition-based String Transduction for Limited-Resource Setting in Morphology (C18-1)
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| Challenge: | Morphological string transduction involves mapping one word form into another, possibly given a feature specification for the mapping. |
| Approach: | They propose a neural transition-based model that uses a simple set of edit actions for morphological transduction tasks such as reinflection and reinflation. |
| Outcome: | The proposed model outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. |
AMR Alignment for Morphologically-rich and Pro-drop Languages (2022.acl-srw)
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| Challenge: | Existing AMR aligners for English are not well suited for many languages where many concepts appear from morphologically-semantic elements. |
| Approach: | They propose to use a tree traversal approach to align AMR concepts from morphemes in a Turkish language. |
| Outcome: | The proposed aligner outperforms the existing aligners for English and Portuguese in terms of precision, recall and F1 score. |
Improving Parallel Sentence Mining for Low-Resource and Endangered Languages (2025.acl-short)
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| Challenge: | Parallel sentence mining is a technique used to find matching sentence pairs from a source and target language. |
| Approach: | They propose a benchmark dataset for parallel sentence mining on three low-resource languages . they apply alignment post-processing and cluster-based isotropy enhancement techniques to one of them . |
| Outcome: | The proposed datasets show better mining quality overall for low-resource languages . the proposed methods are crucial for optimizing parallel data extraction for low resource languages - a new study shows. |
Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data (D19-55)
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| Challenge: | a new corpus of unstructured data from social media is presenting challenges to NLP research . standardisation is neither natural nor universal, it is rather a human invention. |
| Approach: | They compile a parallel corpus of Arabic textual data matched with human annotations . they use a deep neural model designed to deal with context-dependent spelling correction . |
| Outcome: | The proposed model performs best with two CNN sub-network encoders and an LSTM decoder . pre-processing data token-by-token with edit-distance aligner significantly improves performance . |
Inducing and Using Alignments for Transition-based AMR Parsing (2022.naacl-main)
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Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum, Tahira Naseem, Yoon Kim, Ramón Astudillo
| Challenge: | Abstract Meaning Representation parsers rely on node-to-word alignments, but lack the complexity of the pipeline. |
| Approach: | They propose a neural aligner for abstract meaning representation that learns node-to-word alignments without relying on pipelines. |
| Outcome: | The proposed approach improves accuracy and generalization from AMR2.0 to AMR3.0 corpora. |
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model (2024.emnlp-industry)
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Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Tushar Nagarajan, Matt Smith, Shashank Jain, Chun-Fu Yeh, Prakash Murugesan, Peyman Heidari, Yue Liu, Kavya Srinet, Babak Damavandi, Anuj Kumar
| Challenge: | Prior work on LLMs focused on models that combine text and one other modality, such as image encoders or proprietary models that are not open sourced. |
| Approach: | They propose a unified model that reasons over diverse input modality signals and generates textual responses. |
| Outcome: | The proposed model performs better on multimodal tasks than industry-leading models . |
A Structured Syntax-Semantics Interface for English-AMR Alignment (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) annotations do not require explicit mapping between elements of an AMR and the corresponding elements of the sentence that evoke them. |
| Approach: | They devised an expressive framework to align AMR graphs to dependency graphs . their framework explains how 97% of AMR edges are evoked by words or syntax . |
| Outcome: | The proposed framework explains how 97% of AMR edges are evoked by words or syntax. |
Cross-lingual AMR Aligner: Paying Attention to Cross-Attention (2023.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) graphs embed the semantics of a sentence in a directed acyclic graph, where concepts are represented by nodes, semantic relations between concepts by edges, and the co-references by reentrant nodes. |
| Approach: | They propose a novel aligner for Abstract Meaning Representation graphs that scales cross-lingually and can align units and spans in sentences of different languages. |
| Outcome: | The proposed aligner achieves state-of-the-art in the benchmarks and can scale cross-lingually. |
SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings (2020.findings-emnlp)
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| Challenge: | Word alignments are useful for statistical and neural machine translation (NMT) and cross-lingual annotation projection. |
| Approach: | They propose to leverage multilingual word embeddings for word alignment. |
| Outcome: | The proposed methods perform better for four languages and comparable for two languages than traditional statistical aligners even with abundant parallel data. |
Automatic Decomposition of Text Editing Examples into Primitive Edit Operations: Toward Analytic Evaluation of Editing Systems (2024.lrec-main)
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| Challenge: | Existing methods to automate text editing tasks are blackboxed and do not understand the behavior of the systems. |
| Approach: | They propose a task of automatic decomposition of text editing examples into primitive edit operations by using a phrase aligner and a large language model. |
| Outcome: | The proposed method perfectly decomposes 44% and 64% of editing examples . Detailed analyses also provide insights into the difficulties of this task . |
Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation (2020.emnlp-main)
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Tahmid Hasan, Abhik Bhattacharjee, Kazi Samin, Masum Hasan, Madhusudan Basak, M. Sohel Rahman, Rifat Shahriyar
| Challenge: | despite being the seventh most widely spoken language, Bengali has received little attention in machine translation due to being low in resources. |
| Approach: | They propose a customized sentence segmenter for Bengali and two new methods for parallel corpus creation on low-resource setups. |
| Outcome: | The proposed method improves Bengali-English parallel corpus by 9 BLEU over previous approaches . the results will pave the way for future research on Bengali and other low-resource languages . |
Multilingual Sentence Transformer as A Multilingual Word Aligner (2022.findings-emnlp)
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| Challenge: | Multilingual pretrained language models (mPLMs) have shown their effectiveness in multilingual word alignment induction, but these methods usually start from mBERT or XLM-R. |
| Approach: | They propose to fine tune multilingual sentence Transformer LaBSE for alignment induction using parallel corpus and a parallel corpora model. |
| Outcome: | The proposed model outperforms existing models on seven language pairs and achieves new state-of-the-art on zero-shot language pairs. |
Third-Party Aligner for Neural Word Alignments (2022.findings-emnlp)
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| Challenge: | Existing work shows that word alignment can be competitive . |
| Approach: | They propose to use word alignments generated by a third-party word aligner to supervise the neural word alignment training. |
| Outcome: | The proposed approach can find more accurate word alignments and delete wrong alignments, leading to better performance than the current best third-party word aligner. |
Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments (2021.acl-long)
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| Challenge: | Current algorithms for AMR parsing suffer from limited coverage and less-than-ideal accuracy . a new algorithm for AML uses unsupervised learning and heuristics to align components of AMR graphs to spans in English sentences . |
| Approach: | They propose algorithms for aligning components of Abstract Meaning Representation graphs to spans in English sentences. |
| Outcome: | The proposed approach covers a wider variety of AMR substructures than previously considered . it achieves higher coverage of nodes and edges, and does so with higher accuracy. |
Creating a Translation Matrix of the Bible’s Names Across 591 Languages (L18-1)
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| Challenge: | In low-resource languages, the Bible is the only significant bilingual, or even monolingual, text available . standard word alignment tools can be noisy, making downstream tasks difficult . a novel resource of 1129 aligned Bible person and place names is developed . |
| Approach: | They propose to use Bible person and place names as a tool for translation and transliteration . they use weighted edit distance, machine translation-based transliterations and affixal induction and transformation models to improve the Bible's output. |
| Outcome: | The proposed model outperforms a widely used word aligner on 97% of test words on multilingual named-entity alignment and translation across 591 languages. |
An AMR Aligner Tuned by Transition-based Parser (D18-1)
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| Challenge: | Experimental results show that our AMR aligner outperforms the rule-based aligner by achieving higher alignment F1 score and consistently improving two open-source AMR parsers. |
| Approach: | They propose a rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. |
| Outcome: | The proposed AMR aligner outperforms the current state-of-the-art parser by achieving higher alignment F1 score and consistently improving two open-source AMR parsers. |
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment (2021.acl-long)
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| Challenge: | Experimental results show that denoising word alignment improves cross-lingual transferability . most applications and resources are still English-centric, making non-English users hard to access. |
| Approach: | They propose to denoise word alignment as a cross-lingual pre-training task . they first self-label word alignments for parallel sentences and then mask tokens . |
| Outcome: | The proposed model improves cross-lingual transferability on token-level tasks, especially on question answering, and structured prediction. |
Imitation Learning for Neural Morphological String Transduction (D18-1)
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| Challenge: | Recent studies have shown that neural transition-based models can be used for morphological tasks such as inflection generation and lemmatization without a character aligner or warm start. |
| Approach: | They propose to use imitation learning to train a neural transition-based string transducer for morphological tasks such as inflection generation and lemmatization. |
| Outcome: | The proposed model eliminates the need for a character aligner or warm start and achieves state-of-the-art performance on several datasets. |
OTTAWA: Optimal TransporT Adaptive Word Aligner for Hallucination and Omission Translation Errors Detection (2024.findings-acl)
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| Challenge: | Existing methods for detecting hallucinations and omissions in Machine Translation systems focus on analyzing the model’s internal states or relying on external tools. |
| Approach: | They propose an Optimal Transport-based word aligner specifically designed to enhance the detection of hallucinations and omissions in Machine Translation systems. |
| Outcome: | The proposed method is competitive with state-of-the-art methods across 18 language pairs on the HalOmi benchmark and shows promising features. |
HyperNetwork-based Decoupling to Improve Model Generalization for Few-Shot Relation Extraction (2023.emnlp-main)
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| Challenge: | Existing studies cannot generalize well to unseen relations using Prototypical Networks . current approaches are dependent on large amount of labeled data and cannot deal with unseense relations well. |
| Approach: | They propose a HyperNetwork-based Decoupling approach to improve FSRE generalization . they propose FSre models with an encoder, network generator and refined classifiers . |
| Outcome: | The proposed method improves the generalization of few-shot relation extraction models. |
Broad-Coverage Semantic Parsing as Transduction (D19-1)
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| Challenge: | Existing approaches to broad-coverage semantic parsing are not applicable to all frameworks because of the lack of explicit alignments between tokens in the sentence and nodes in the semantic graph. |
| Approach: | They propose a transduction parsing paradigm that unifies different broad-coverage semantic parsers into a paradigm that leverages multiple attention mechanisms to build meaning representation. |
| Outcome: | The proposed approach improves state-of-the-art on AMR, SDP and UCCA and is competitive with the state- of-the art on SDP. |
StyleDubber: Towards Multi-Scale Style Learning for Movie Dubbing (2024.findings-acl)
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Gaoxiang Cong, Yuankai Qi, Liang Li, Amin Beheshti, Zhedong Zhang, Anton Hengel, Ming-Hsuan Yang, Chenggang Yan, Qingming Huang
| Challenge: | Existing methods for movie dubbing break phonemes in scripts, resulting in incomplete phoneme pronunciation and poor identity stability. |
| Approach: | They propose a method that switches dubbing learning from frame level to phoneme level . it uses a multimodal style adaptor to learn pronunciation style from audio . |
| Outcome: | The proposed method improves on two benchmarks, V2C and Grid, and is available on github. |
PAT: Parameter-Free Audio-Text Aligner to Boost Zero-Shot Audio Classification (2025.naacl-long)
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| Challenge: | Audio-Language Models (ALMs) have demonstrated remarkable performance in zero-shot audio classification. |
| Approach: | They propose a training-free method that enhances audio and language representations using mutual feedback. |
| Outcome: | The proposed method outperforms vanilla zero-shot evaluation with significant margins of 0.42%-27.0%. |
Graph Algorithms for Multiparallel Word Alignment (2021.emnlp-main)
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Ayyoob ImaniGooghari, Masoud Jalili Sabet, Lutfi Kerem Senel, Philipp Dufter, François Yvon, Hinrich Schütze
| Challenge: | Word alignments are useful for typological research and can be used in machine translation systems. |
| Approach: | They propose to exploit the multiparallelity of parallel corpora by representing bilingual alignments as a graph and then predicting additional edges. |
| Outcome: | The proposed algorithm improves the accuracy of bilingual alignments by 28% over baseline algorithms. |
Stark: Social Long-Term Multi-Modal Conversation with Persona Commonsense Knowledge (2024.findings-emnlp)
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| Challenge: | Existing studies focus on image-sharing behavior in singular sessions, leading to limited long-term social interaction. |
| Approach: | They propose a large-scale long-term multi-modal dialogue dataset that generates long-time multi-modity dialogue distilled from ChatGPT and proposed image aligner. |
| Outcome: | The proposed framework generates long-term multi-modal dialogue from ChatGPT and image aligner. |
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. |
PEIT: Bridging the Modality Gap with Pre-trained Models for End-to-End Image Translation (2023.acl-long)
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| Challenge: | Image translation is a task that translates an image containing text in the source language to the target language. |
| Approach: | They propose an end-to-end image translation framework that bridges the modality gap between visual inputs and textual inputs/outputs of machine translation (MT). |
| Outcome: | The proposed framework outperforms existing models on a large-scale image translation corpus . it significantly outperformed both cascaded and strong models on the e-commerce domain . |
Aligners: Decoupling LLMs and Alignment (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) need to be aligned with human expectations to ensure their safety and utility in most applications. |
| Approach: | They propose to decouple LLMs and alignment by training *aligner* models that can be used to align any LLM on an as-needed basis. |
| Outcome: | The proposed model can be used to align any LLM for a given criteria on an as-needed basis. |
TransAlign: Machine Translation Encoders are Strong Word Aligners, Too (2025.findings-emnlp)
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| Challenge: | translation-based approaches to cross-lingual transfer (XLT) are limited. |
| Approach: | They propose a word aligner that utilizes the encoder of a massively multilingual MT model. |
| Outcome: | The proposed word aligner outperforms existing WA and state-of-the-art non-WA-based methods in token classification tasks. |