Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userLearning to Synthesize Data for Semantic Parsing
تعلم توليف البيانات لتحليل الدلالي
550
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
اكتسبت توليف البيانات لتحليل الدلالي اهتماما متزايدا مؤخرا. ومع ذلك، فإن معظم الطرق تتطلب قواعد يدوية (عالية الدقة) في عملية توليدها، مما يعوق استكشاف بيانات غير مرئية متنوعة. في هذا العمل، نقترح نموذجا عاما يتميز ببرنامج PCFG (غير العصبي) نماذج تكوين البرامج (E.G.، SQL)، ونموذج الترجمة المستندة إلى BART خرائط برنامج إلى كلام. نظرا لبساطة PCFG و BART المدربة مسبقا، يمكن تعلم نموذجنا التوليدي بكفاءة من البيانات الموجودة في متناول اليد. علاوة على ذلك، يؤدي التركيبات النمذجة بشكل صريح باستخدام PCFG إلى استكشاف أفضل لبرامج غير مرئية، وبالتالي توليد بيانات أكثر تنوعا. نقوم بتقييم طريقتنا في كل من الإعدادات داخل المجال والخروج من تحليل النص إلى SQL على المعايير القياسية للجهازية والعنكب العنكبوت، على التوالي. تبين نتائجنا التجريبية أن البيانات المركبة التي تم إنشاؤها من طرازنا يمكن أن تساعد بشكل كبير في محلل الدلالي يحقق تعميم أفضل أو مجال.
Synthesizing data for semantic parsing has gained increasing attention recently. However, most methods require handcrafted (high-precision) rules in their generative process, hindering the exploration of diverse unseen data. In this work, we propose a generative model which features a (non-neural) PCFG that models the composition of programs (e.g., SQL), and a BART-based translation model that maps a program to an utterance. Due to the simplicity of PCFG and pre-trained BART, our generative model can be efficiently learned from existing data at hand. Moreover, explicitly modeling compositions using PCFG leads to better exploration of unseen programs, thus generate more diverse data. We evaluate our method in both in-domain and out-of-domain settings of text-to-SQL parsing on the standard benchmarks of GeoQuery and Spider, respectively. Our empirical results show that the synthesized data generated from our model can substantially help a semantic parser achieve better compositional and domain generalization.
References used
https://aclanthology.org/
rate research
Read More
AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they require explicit annotations of t
he compositional tree structures for training. In the past, these were obtained using complex graphbank-specific heuristics written by experts. Here we show how they can instead be trained directly on the graphs with a neural latent-variable model, drastically reducing the amount and complexity of manual heuristics. We demonstrate that our model picks up on several linguistic phenomena on its own and achieves comparable accuracy to supervised training, greatly facilitating the use of AM dependency parsing for new sembanks.
Humans are capable of learning novel concepts from very few examples; in contrast, state-of-the-art machine learning algorithms typically need thousands of examples to do so. In this paper, we propose an algorithm for learning novel concepts by repre
senting them as programs over existing concepts. This way the concept learning problem is naturally a program synthesis problem and our algorithm learns from a few examples to synthesize a program representing the novel concept. In addition, we perform a theoretical analysis of our approach for the case where the program defining the novel concept over existing ones is context-free. We show that given a learned grammar-based parser and a novel production rule, we can augment the parser with the production rule in a way that provably generalizes. We evaluate our approach by learning concepts in the semantic parsing domain extended to the few-shot novel concept learning setting, showing that our approach significantly outperforms end-to-end neural semantic parsers.
In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing. Instead of predicting a probability, our model predicts a real-valued score at each step and does not suffer from the label bias problem. Exp
eriments show that our approach outperforms locally normalized models on small datasets, but it does not yield improvement on a large dataset.
Semantic parsing aims at translating natural language (NL) utterances onto machine-interpretable programs, which can be executed against a real-world environment. The expensive annotation of utterance-program pairs has long been acknowledged as a maj
or bottleneck for the deployment of contemporary neural models to real-life applications. In this work, we focus on the task of semi-supervised learning where a limited amount of annotated data is available together with many unlabeled NL utterances. Based on the observation that programs which correspond to NL utterances should always be executable, we propose to encourage a parser to generate executable programs for unlabeled utterances. Due to the large search space of executable programs, conventional methods that use beam-search for approximation, such as self-training and top-k marginal likelihood training, do not perform as well. Instead, we propose a set of new training objectives that are derived by approaching the problem of learning from executions from the posterior regularization perspective. Our new objectives outperform conventional methods on Overnight and GeoQuery, bridging the gap between semi-supervised and supervised learning.
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders. In this work, we explore an alternative paradigm. We formulate semantic
parsing as a dependency parsing task, applying graph-based decoding techniques developed for syntactic parsing. We compare various decoding techniques given the same pre-trained Transformer encoder on the TOP dataset, including settings where training data is limited or contains only partially-annotated examples. We find that our graph-based approach is competitive with sequence decoders on the standard setting, and offers significant improvements in data efficiency and settings where partially-annotated data is available.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
