Papers by Adhiguna Kuncoro
Scalable Syntax-Aware Language Models Using Knowledge Distillation (P19-1)
Copied to clipboard
| Challenge: | Prior work has shown that syntactic neural language models learn from small amounts of training data more effectively than sequential models. |
| Approach: | They propose a knowledge distillation technique that transfers knowledge from a syntactic language model trained on a small corpus to an LSTM language model and enables it to develop a more structurally sensitive representation of the larger training data. |
| Outcome: | The proposed method improves on baseline syntactic evaluations on LSTMs with a higher level of accuracy than previous methods. |
A Systematic Investigation of Commonsense Knowledge in Large Language Models (2022.emnlp-main)
Copied to clipboard
Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann, Cyprien de Masson d’Autume, Phil Blunsom, Aida Nematzadeh
| Challenge: | Recent large language models (LMs) have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup. |
| Approach: | They conduct a systematic and rigorous zero-shot and few-shot commonsense evaluation of large pre-trained language models to better understand their ability to capture commonsensical knowledge. |
| Outcome: | The proposed model can exploit surface cues and annotation artefacts without task-specific supervision and is insufficient to achieve human-level commonsense performance. |
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation (2021.emnlp-main)
Copied to clipboard
Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, Karissa Vincentio, Xiaohong Li, Adhiguna Kuncoro, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Khodra, Ayu Purwarianti, Pascale Fung
| Challenge: | Lack of publicly available NLG benchmarks for low-resource languages poses a challenge . authors show that IndoBART and IndoGPT achieve competitive performance on all tasks . |
| Approach: | They propose a benchmark to measure natural language generation progress in three low-resource languages of Indonesia . they use a corpus of pretraining datasets to build their models . |
| Outcome: | The proposed benchmark measures progress in Indonesian, Javanese, and Sundanese . the results highlight the importance of pretraining on closely related, localized languages . |
Syntactic Structure Distillation Pretraining for Bidirectional Encoders (2020.tacl-1)
Copied to clipboard
Adhiguna Kuncoro, Lingpeng Kong, Daniel Fried, Dani Yogatama, Laura Rimell, Chris Dyer, Phil Blunsom
| Challenge: | Textual representation learners trained on large amounts of data have been successful on downstream tasks. |
| Approach: | They propose a knowledge distillation strategy for injecting syntactic biases into BERT pretraining by distilling the approximate marginal distribution over words in context from the syntaktic LM. |
| Outcome: | The proposed method reduces relative error by 2–21% on a diverse set of structured prediction tasks. |
Text Genre and Training Data Size in Human-like Parsing (D19-1)
Copied to clipboard
| Challenge: | Using domain-specific training, NLP systems work better, but only when the training examples come from the same textual genre. |
| Approach: | They relate the states of a neural phrase-structure parser to electrophysiological measures from human participants. |
| Outcome: | The proposed model is well-matched to the training data from human participants, but only when the training examples come from the same genre. |
Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale (2022.tacl-1)
Copied to clipboard
| Challenge: | a novel class of Transformer language models that combine expressive power, scalability, and strong performance of Transformers and recursive syntactic compositions. |
| Approach: | They introduce Transformer Grammars, a class of Transformer language models that combine expressive power and recursive syntactic compositions. |
| Outcome: | The proposed model outperforms strong baselines on sentence-level language modeling perplexity and syntax-sensitive language evaluation metrics. |
Finding syntax in human encephalography with beam search (P18-1)
Copied to clipboard
| Challenge: | RNNGs are generative models of (tree , string ) pairs that evaluate derivational choices . a non-syntactic neural language model yields no reliable effects . |
| Approach: | They propose to combine a probabilistic generative grammar with a parsing procedure that uses it to manage syntactic derivations as it advances from one word to the next. |
| Outcome: | The proposed model derives two amplitude effects when used against human encephalography data. |
LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better (P18-1)
Copied to clipboard
| Challenge: | a recent study found that language models fail to learn long-range syntax sensitive dependencies. |
| Approach: | They propose to use a subject-verb agreement diagnostic to determine whether language models can learn long-range syntax sensitive dependencies. |
| Outcome: | The proposed model outperforms left-corner and bottom-up variants in learning non-local dependencies. |
A Natural Bias for Language Generation Models (2023.acl-short)
Copied to clipboard
| Challenge: | a standard probabilistic model for language generation has likely not yet learnt many semantic or syntactic rules of natural language, making it difficult to estimate the probability distribution over next tokens. |
| Approach: | They propose to initialise bias terms in a model's final linear layer with the log-unigram distribution and use it to output the unigram frequency statistics as prior knowledge. |
| Outcome: | The proposed method improves learning efficiency and improves overall performance. |
On “Scientific Debt” in NLP: A Case for More Rigour in Language Model Pre-Training Research (2023.acl-long)
Copied to clipboard
Made Nindyatama Nityasya, Haryo Wibowo, Alham Fikri Aji, Genta Winata, Radityo Eko Prasojo, Phil Blunsom, Adhiguna Kuncoro
| Challenge: | Despite rapid recent progress, current research practices conflate different sources of model improvement without conducting proper ablation studies and principled comparisons . authors conclude with recommendations for how to encourage and incentivize this line of work . |
| Approach: | They critique current research practices in the field of language model pre-training . they examine the success of language models pre-trained on large amounts of data . |
| Outcome: | The proposed models can achieve competitive or better performance than BERT under comparable conditions. |
Unsupervised Recurrent Neural Network Grammars (N19-1)
Copied to clipboard
| Challenge: | RNNGs model syntax and structure by incrementally generating a syntax tree and sentence in a top-down, left-to-right order. |
| Approach: | They explore unsupervised learning of recurrent neural network grammars for language modeling and grammar induction. |
| Outcome: | The proposed model outperforms standard sequential language models and improves parsing performance. |