Iftekhar Naim


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Transforming Sequence Tagging Into A Seq2Seq Task
Karthik Raman | Iftekhar Naim | Jiecao Chen | Kazuma Hashimoto | Kiran Yalasangi | Krishna Srinivasan
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Pretrained, large, generative language models (LMs) have had great success in a wide range of sequence tagging and structured prediction tasks. Casting a sequence tagging task as a Seq2Seq one requires deciding the formats of the input and output sequences. However, we lack a principled understanding of the trade-offs associated with these formats (such as the effect on model accuracy, sequence length, multilingual generalization, hallucination). In this paper, we rigorously study different formats one could use for casting input text sentences and their output labels into the input and target (i.e., output) of a Seq2Seq model. Along the way, we introduce a new format, which we show to to be both simpler and more effective. Additionally the new format demonstrates significant gains in the multilingual settings – both zero-shot transfer learning and joint training. Lastly, we find that the new format is more robust and almost completely devoid of hallucination – an issue we find common in existing formats. With well over a 1000 experiments studying 14 different formats, over 7 diverse public benchmarks – including 3 multilingual datasets spanning 7 languages – we believe our findings provide a strong empirical basis in understanding how we should tackle sequence tagging tasks.


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Feature-Based Decipherment for Machine Translation
Iftekhar Naim | Parker Riley | Daniel Gildea
Computational Linguistics, Volume 44, Issue 3 - September 2018

Orthographic similarities across languages provide a strong signal for unsupervised probabilistic transduction (decipherment) for closely related language pairs. The existing decipherment models, however, are not well suited for exploiting these orthographic similarities. We propose a log-linear model with latent variables that incorporates orthographic similarity features. Maximum likelihood training is computationally expensive for the proposed log-linear model. To address this challenge, we perform approximate inference via Markov chain Monte Carlo sampling and contrastive divergence. Our results show that the proposed log-linear model with contrastive divergence outperforms the existing generative decipherment models by exploiting the orthographic features. The model both scales to large vocabularies and preserves accuracy in low- and no-resource contexts.


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Discriminative Unsupervised Alignment of Natural Language Instructions with Corresponding Video Segments
Iftekhar Naim | Young C. Song | Qiguang Liu | Liang Huang | Henry Kautz | Jiebo Luo | Daniel Gildea
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Sliding Alignment Windows for Real-Time Crowd Captioning
Mohammad Kazemi | Rahman Lavaee | Iftekhar Naim | Daniel Gildea
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Text Alignment for Real-Time Crowd Captioning
Iftekhar Naim | Daniel Gildea | Walter Lasecki | Jeffrey P. Bigham
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies