update broken links (NVIDIA#8079) (NVIDIA#8080)

Co-authored-by: Nithin Rao <[email protected]>

docs/source/asr/asr_language_modeling.rst

@@ -440,8 +440,7 @@ works in lexicon decoding mode, it does not work in lexicon-free mode. Word boos
 such that you can manually increase or decrease the probability of emitting certain words. This can be very helpful if you have certain
 uncommon or industry-specific words which you want to ensure transcribe correctly.
 
-For more information on word boosting, see `here <https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-customizing.html#word-boosting>`__
-and `here <https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-customizing.html#word-boosting>`__
+For more information on word boosting, `here <https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-customizing.html#word-boosting>`__
 
 In order to use word boosting in Nemo, you need to create a simple tab-separated text file which contains each word to be boosted, followed by
 tab, and then the boosted score for that word.

docs/source/nlp/joint_intent_slot.rst

@@ -233,7 +233,7 @@ Model Evaluation and Inference
 There is no separate script for the evaluation and inference of this model in NeMo, however, inside of the example file `examples/nlp/intent_slot_classification/intent_slot_classification.py` 
 after the training part is finished, you can see the code that evaluates the trained model on an evaluation test set and then an example of doing inference using a list of given queries.
 
-For the deployment in the production environment, refer to `NVIDIA Riva <https://developer.nvidia.com/nvidia-riva-getting-started>`__ and `NVIDIA TLT documentation <https://docs.nvidia.com/metropolis/TLT/tlt-user-guide/text/nlp/index.html>`__.
+For the deployment in the production environment, refer to `NVIDIA Riva <https://docs.nvidia.com/deeplearning/riva/user-guide/docs/quick-start-guide.html>`__ and `NVIDIA TLT documentation <https://docs.nvidia.com/metropolis/TLT/tlt-user-guide/text/nlp/index.html>`__.
 
 References
 ----------

docs/source/nlp/token_classification.rst

@@ -68,7 +68,7 @@ Dataset Conversion
 ------------------
 
 To convert an `IOB format <https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)>`__ (short for inside, outside, beginning) data to the format required for training, use
-`examples/nlp/token_classification/data/import_from_iob_format.py <https://github.com/NVIDIA/NeMo/blob/stable/examples/nlp/token_classification/data/import_from_iob_format.py)>`_.
+`examples/nlp/token_classification/data/import_from_iob_format.py <https://github.com/NVIDIA/NeMo/blob/stable/examples/nlp/token_classification/data/import_from_iob_format.py>`_.
 
 .. code::