Papers with English sentence
Realizing Universal Dependencies Structures (D19-63)
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| Challenge: | UD-SURFR is a symbolic surface realizer for Universal Dependencies structures . it is based on a tree of constituents that is then converted into an English sentence . |
| Approach: | They propose a symbolic system that transforms a dependency tree into a tree of constituents that is transformed into an English sentence by an existing realizer. |
| Outcome: | The proposed system is quite fast and showed competitive results for English sentences using automatic and manual evaluation measures. |
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World (2021.acl-long)
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Rowan Zellers, Ari Holtzman, Matthew Peters, Roozbeh Mottaghi, Aniruddha Kembhavi, Ali Farhadi, Yejin Choi
| Challenge: | PIGLeT model learns physical commonsense knowledge through interaction, then uses this knowledge to ground language. |
| Approach: | They propose a model that learns physical commonsense knowledge through interaction . they factorize PIGLeT into a physical dynamics model and a separate language model . |
| Outcome: | The proposed model outperforms a 100x larger, text-to-text approach in forecasting language . it can read a sentence, simulate neurally what might happen next, and communicate that result through a literal symbolic representation, or natural language. |
A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification (2021.findings-acl)
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| Challenge: | Existing models that generate generic simplified outputs for a given source text have been used to specify output properties. |
| Approach: | They propose a non-autoregressive model that iteratively edits an input sequence and incorporates lexical complexity information into the refinement process to generate simplifications that better match the desired output complexity. |
| Outcome: | The proposed model incorporates lexical complexity information into the refinement process to achieve more complex simplification operations such as content deletion and paraphrasing, as well as sentence splitting. |
Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages (2023.acl-long)
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Arnav Mhaske, Harshit Kedia, Sumanth Doddapaneni, Mitesh M. Khapra, Pratyush Kumar, Rudra Murthy, Anoop Kunchukuttan
| Challenge: | Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP). |
| Approach: | They present the largest publicly available Named Entity Recognition dataset for the 11 major Indian languages from two language families. |
| Outcome: | The proposed dataset is the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. |