Update README.md

README.md

@@ -13,7 +13,7 @@ metrics:
 
 # End-to-end SLU model for Timers and Such
 
-Attention-based RNN sequence-to-sequence model for [Timers and Such](https://zenodo.org/record/4623772) trained on the `train-real` subset. This model checkpoint achieves 86.7% accuracy on `test-real`.
+Attention-based RNN sequence-to-sequence model for [Timers and Such](https://arxiv.org/abs/2104.01604) trained on the `train-real` subset. This model checkpoint achieves 86.7% accuracy on `test-real`.
 
 The model uses an ASR model trained on LibriSpeech ([`speechbrain/asr-crdnn-rnnlm-librispeech`](https://huggingface.co/speechbrain/asr-crdnn-rnnlm-librispeech)) to extract features from the input audio, then maps these features to an intent and slot labels using a beam search. 
 
@@ -35,7 +35,7 @@ title = {SpeechBrain},
 year = {2021},
 publisher = {GitHub},
 journal = {GitHub repository},
-howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
+howpublished = {\\\\\\\\url{https://github.com/speechbrain/speechbrain}},
 }
 ```