Time-Stamped Language Model: Teaching Language Models to Understand the Flow of Events

• URL: http://arxiv.org/abs/2104.07635v1
• Date: Thu, 15 Apr 2021 17:50:41 GMT
• Title: Time-Stamped Language Model: Teaching Language Models to Understand the Flow of Events
• Authors: Hossein Rajaby Faghihi and Parisa Kordjamshidi
• Abstract summary: We propose to formulate this task as a question answering problem. This enables us to use pre-trained language models on other QA benchmarks by adapting those to the procedural text understanding. Our model evaluated on the Propara dataset shows improvements on the published state-of-the-art results with a $3.1%$ increase in F1 score.
• Score: 8.655294504286635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tracking entities throughout a procedure described in a text is challenging due to the dynamic nature of the world described in the process. Firstly, we propose to formulate this task as a question answering problem. This enables us to use pre-trained transformer-based language models on other QA benchmarks by adapting those to the procedural text understanding. Secondly, since the transformer-based language models cannot encode the flow of events by themselves, we propose a Time-Stamped Language Model~(TSLM model) to encode event information in LMs architecture by introducing the timestamp encoding. Our model evaluated on the Propara dataset shows improvements on the published state-of-the-art results with a $3.1\%$ increase in F1 score. Moreover, our model yields better results on the location prediction task on the NPN-Cooking dataset. This result indicates that our approach is effective for procedural text understanding in general.

Related papers

• FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction [49.510163437116645]
  Click-through rate (CTR) prediction plays as a core function module in personalized online services. Traditional ID-based models for CTR prediction take as inputs the one-hot encoded ID features of tabular modality. Pretrained Language Models(PLMs) has given rise to another paradigm, which takes as inputs the sentences of textual modality. We propose to conduct Fine-grained feature-level ALignment between ID-based Models and Pretrained Language Models(FLIP) for CTR prediction.
  arXiv  Detail & Related papers  (2023-10-30T11:25:03Z)
• Instruction Position Matters in Sequence Generation with Large Language Models [67.87516654892343]
  Large language models (LLMs) are capable of performing conditional sequence generation tasks, such as translation or summarization. We propose enhancing the instruction-following capability of LLMs by shifting the position of task instructions after the input sentences.
  arXiv  Detail & Related papers  (2023-08-23T12:36:57Z)
• Coalescing Global and Local Information for Procedural Text Understanding [70.10291759879887]
  A complete procedural understanding solution should combine three core aspects: local and global views of the inputs, and global view of outputs. In this paper, we propose Coalescing Global and Local InformationCG, a new model that builds entity and time representations. Experiments on a popular procedural text understanding dataset show that our model achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2022-08-26T19:16:32Z)
• Temporal Attention for Language Models [24.34396762188068]
  We extend the key component of the transformer architecture, i.e., the self-attention mechanism, and propose temporal attention. temporal attention can be applied to any transformer model and requires the input texts to be accompanied with their relevant time points. We leverage these representations for the task of semantic change detection. Our proposed model achieves state-of-the-art results on all the datasets.
  arXiv  Detail & Related papers  (2022-02-04T11:55:34Z)
• CoreLM: Coreference-aware Language Model Fine-Tuning [0.0]
  We propose a Fine-Tuning framework, named CoreLM, that extends the architecture of current Pretrained Language Models. We make available information outside the contextual space of the model, which results in a better Language Model for a fraction of the computational cost. Our proposed model achieves a lower Perplexity in GUMBY and LAMBDADA datasets when compared to GPT2 and a fine-tuned version of GPT2 without any changes.
  arXiv  Detail & Related papers  (2021-11-04T08:44:31Z)
• Sequence-to-Sequence Lexical Normalization with Multilingual Transformers [3.3302293148249125]
  Current benchmark tasks for natural language processing contain text that is qualitatively different from the text used in informal day to day digital communication. This discrepancy has led to severe performance degradation of state-of-the-art NLP models when fine-tuned on real-world data. We propose a sentence-level sequence-to-sequence model based on mBART, which frames the problem as a machine translation problem.
  arXiv  Detail & Related papers  (2021-10-06T15:53:20Z)
• Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training [86.91380874390778]
  We present Generation-Augmented Pre-training (GAP), that jointly learns representations of natural language utterances and table schemas by leveraging generation models to generate pre-train data. Based on experimental results, neural semantics that leverage GAP MODEL obtain new state-of-the-art results on both SPIDER and CRITERIA-TO-generative benchmarks.
  arXiv  Detail & Related papers  (2020-12-18T15:53:50Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)
• POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training [93.79766670391618]
  We present POINTER, a novel insertion-based approach for hard-constrained text generation. The proposed method operates by progressively inserting new tokens between existing tokens in a parallel manner. The resulting coarse-to-fine hierarchy makes the generation process intuitive and interpretable.
  arXiv  Detail & Related papers  (2020-05-01T18:11:54Z)
• Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling [4.525267347429154]
  We train a Transformer-based neural model on the BERT language model. In addition, we propose a new method of BERT-windowing, which allows chunk-wise processing of texts longer than the BERT window size. The results of our models are compared to a baseline and the state-of-the-art models on the CNN/Daily Mail dataset.
  arXiv  Detail & Related papers  (2020-03-29T14:00:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.