19 research outputs found
Ratatouille: A tool for Novel Recipe Generation
Due to availability of a large amount of cooking recipes online, there is a
growing interest in using this as data to create novel recipes. Novel Recipe
Generation is a problem in the field of Natural Language Processing in which
our main interest is to generate realistic, novel cooking recipes. To come up
with such novel recipes, we trained various Deep Learning models such as LSTMs
and GPT-2 with a large amount of recipe data. We present Ratatouille
(https://cosylab.iiitd.edu.in/ratatouille2/), a web based application to
generate novel recipes.Comment: 4 pages, 5 figures, 38th IEEE International Conference on Data
Engineering, DECOR Worksho
On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification
Implicit discourse relation classification is one of the most difficult parts
in shallow discourse parsing as the relation prediction without explicit
connectives requires the language understanding at both the text span level and
the sentence level. Previous studies mainly focus on the interactions between
two arguments. We argue that a powerful contextualized representation module, a
bilateral multi-perspective matching module, and a global information fusion
module are all important to implicit discourse analysis. We propose a novel
model to combine these modules together. Extensive experiments show that our
proposed model outperforms BERT and other state-of-the-art systems on the PDTB
dataset by around 8% and CoNLL 2016 datasets around 16%. We also analyze the
effectiveness of different modules in the implicit discourse relation
classification task and demonstrate how different levels of representation
learning can affect the results.Comment: Accepted by IJCAI 202
Modeling Coherence for Discourse Neural Machine Translation
Discourse coherence plays an important role in the translation of one text.
However, the previous reported models most focus on improving performance over
individual sentence while ignoring cross-sentence links and dependencies, which
affects the coherence of the text. In this paper, we propose to use discourse
context and reward to refine the translation quality from the discourse
perspective. In particular, we generate the translation of individual sentences
at first. Next, we deliberate the preliminary produced translations, and train
the model to learn the policy that produces discourse coherent text by a reward
teacher. Practical results on multiple discourse test datasets indicate that
our model significantly improves the translation quality over the
state-of-the-art baseline system by +1.23 BLEU score. Moreover, our model
generates more discourse coherent text and obtains +2.2 BLEU improvements when
evaluated by discourse metrics.Comment: Accepted by AAAI201