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Found 20 papers, 14 papers with code
The University of Edinburgh’s English-German and English-Hausa Submissions to the WMT21 News Translation Task
This paper presents the University of Edinburgh’s constrained submissions of English-German and English-Hausa systems to the WMT 2021 shared task on news translation.
The University of Edinburgh’s Bengali-Hindi Submissions to the WMT21 News Translation Task
We describe the University of Edinburgh’s Bengali\leftrightarrowHindi constrained systems submitted to the WMT21 News Translation task.
Efficient Machine Translation with Model Pruning and Quantization
We participated in all tracks of the WMT 2021 efficient machine translation task: single-core CPU, multi-core CPU, and GPU hardware with throughput and latency conditions.
Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions
This paper presents a winning submission to the SemEval 2022 Task 1 on two sub-tasks: reverse dictionary and definition modelling.
Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice?
Traditionally, success in multilingual machine translation can be attributed to three key factors in training data: large volume, diverse translation directions, and high quality.
Lucky 52: How Many Languages Are Needed to Instruction Fine-Tune Large Language Models?
Fine-tuning large language models for multilingual downstream tasks requires a diverse set of languages to capture the nuances and structures of different linguistic contexts effectively.
UniArk: Improving Generalisation and Consistency for Factual Knowledge Extraction through Debiasing
We hypothesize that simultaneously debiasing these objectives can be the key to generalisation over unseen prompts.
Fine-tuning Large Language Models with Sequential Instructions
Targeting the scarcity of sequential instructions in present-day data, we propose sequential instruction tuning, a simple yet effective strategy to automatically augment instruction tuning data and equip LLMs with the ability to execute multiple sequential instructions.
EEE-QA: Exploring Effective and Efficient Question-Answer Representations
Current approaches to question answering rely on pre-trained language models (PLMs) like RoBERTa.
Large Language Model Inference with Lexical Shortlisting
Large language model (LLM) inference is computation and memory intensive, so we adapt lexical shortlisting to it hoping to improve both.
Terminology-Aware Translation with Constrained Decoding and Large Language Model Prompting
Terminology correctness is important in the downstream application of machine translation, and a prevalent way to ensure this is to inject terminology constraints into a translation system.
Towards Effective Disambiguation for Machine Translation with Large Language Models
Resolving semantic ambiguity has long been recognised as a central challenge in the field of Machine Translation.
Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca
Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants.
Iterative Translation Refinement with Large Language Models
In this paper, we propose iterative translation refinement to leverage the power of large language models for more natural translation and post-editing.
PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India
This paper introduces PMIndiaSum, a multilingual and massively parallel summarization corpus focused on languages in India.
Exploring Data Augmentation for Code Generation Tasks
Advances in natural language processing, such as transfer learning from pre-trained language models, have impacted how models are trained for programming language tasks too.
The University of Edinburgh's Submission to the WMT22 Code-Mixing Shared Task (MixMT)
For subtask 1 we explored the effects of constrained decoding on English and transliterated subwords in order to produce Hinglish.
To Adapt or to Fine-tune: A Case Study on Abstractive Summarization
Recent advances in the field of abstractive summarization leverage pre-trained language models rather than train a model from scratch.
A Unified Model for Reverse Dictionary and Definition Modelling
We build a dual-way neural dictionary to retrieve words given definitions, and produce definitions for queried words.
The Highs and Lows of Simple Lexical Domain Adaptation Approaches for Neural Machine Translation
Machine translation systems are vulnerable to domain mismatch, especially in a low-resource scenario.
